Distinguishing methylation levels in complex biological samples

ABSTRACT

Provided herein is a method for distinguishing an aberrant methylation level for DNA from a first cell type, including steps of (a) providing a test data set that includes (i) methylation states for a plurality of sites from test genomic DNA from at least one test organism, and (ii) coverage at each of the sites for detection of the methylation states; (b) providing methylation states for the plurality of sites in reference genomic DNA from one or more reference individual organisms, (c) determining, for each of the sites, the methylation difference between the test genomic DNA and the reference genomic DNA, thereby providing a normalized methylation difference for each site; and (d) weighting the normalized methylation difference for each site by the coverage at each of the sites, thereby determining an aggregate coverage-weighted normalized methylation difference score. Also provided herein are sensitive methods for using genomic DNA methylation levels to distinguish cancer cells from normal cells and to classify different cancer types according to their tissues of origin.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser. No. 62/401,591, filed Sep. 29, 2016, and U.S. Provisional Application Ser. No. 62/268,961, filed Dec. 17, 2015, each of which is incorporated by reference herein.

BACKGROUND

The present disclosure relates to determination of methylation patterns in genomic DNA. Specific embodiments relate to prediction, diagnosis, prognosis and monitoring of various conditions based on genomic methylation patterns.

Changes in cellular genetic information, such as mutations in gene sequences which can affect gene expression and/or protein sequence, are associated with many diseases and conditions. However, changes can also occur to genes that affect gene expression; changes caused by mechanisms other than genetic mutations. Epigenetics is the study of changes in gene expression caused by mechanisms other than changes in the underlying DNA sequence, the methylation of DNA being one of those mechanisms. Methylation of DNA, for example, the addition of a methyl group to the 5 position of a cytosine pyrimidine ring or the positional sixth nitrogen of an adenine purine ring, is widespread and plays a critical role in the regulation of gene expression in development and differentiation of diseases such as multiple sclerosis, diabetes, schizophrenia, aging, and cancers. In adult somatic cells, DNA methylation typically occurs in regions where a cytosine nucleotide (C) is found next to a guanine nucleotide (G) where the C and G are linked by a phosphate group (p), the linear construct being referred to as a “CpG” site. Methylation in particular gene regions, for example, in gene promoter regions, can augment or inhibit the expression of these genes.

DNA methylation is widespread and plays a critical role in the regulation of gene expression in development, differentiation and disease. Methylation in particular regions of genes, for example their promoter regions, can inhibit the expression of these genes (Baylin and Herman (2000) DNA hypermethylation in tumorigenesis: epigenetics joins genetics. Trends Genet, 16, 168-174.; Jones and Laird (1999) Cancer epigenetics comes of age. Nat Genet, 21, 163-167.). Gene silencing effects of methylated regions has been shown to be accomplished through the interaction of methylcytosine binding proteins with other structural compounds of the chromatin (Razin (1998) CpG methylation, chromatin structure and gene silencing—a three-way connection. Embo J, 17, 4905-4908.; Yan et al. (2001) Role of DNA methylation and histone acetylation in steroid receptor expression in breast cancer. J Mammary Gland Biol Neoplasia, 6, 183-192.), which, in turn, makes the DNA inaccessible to transcription factors through histone deacetylation and chromatin structure changes (Bestor (1998) Gene silencing. Methylation meets acetylation. Nature, 393, 311-312.). Genomic imprinting in which imprinted genes are preferentially expressed from either the maternal or paternal allele also involves DNA methylation. Deregulation of imprinting has been implicated in several developmental disorders (Kumar (2000) Rett and ICF syndromes: methylation moves into medicine. J Biosci, 25, 213-214.; Sasaki et al. (1993) DNA methylation and genomic imprinting in mammals. Exs, 64, 469-486.; Zhong et al. (1996) A survey of FRAXE allele sizes in three populations. Am J Med Genet, 64, 415-419.). The references cited above are incorporated herein by reference.

In vertebrates, the DNA methylation pattern is established early in embryonic development and in general the distribution of 5-methylcytosine (5 mC) along the chromosome is maintained during the life span of the organism (Razin and Cedar (1993) DNA methylation and embryogenesis. Exs, 64, 343-357.; Reik et al. (2001) Epigenetic reprogramming in mammalian development. Science, 293, 1089-1093, each of which is incorporated herein by reference). Stable transcriptional silencing is important for normal development, and is associated with several epigenetic modifications. If methylation patterns are not properly established or maintained, various disorders like mental retardation, immune deficiency and sporadic or inherited cancers may follow.

Changes in DNA methylation have been recognized as one of the most common molecular alterations in human neoplasia. Hypermethylation of CpG sites located in promoter regions of tumor suppressor genes is a frequent mechanism for gene inactivation in cancers. Hypomethylation of genomic DNA are observed in tumor cells. Further, a correlation between hypomethylation and increased gene expression has been reported for many oncogenes. Monitoring global changes in methylation pattern has been applied to molecular classification of cancers, for example, gene hypermethylation has been associated with clinical risk groups in neuroblastoma and hormone receptor status correlation with response to tamoxifen in breast cancer.

In addition to playing an important role in cancer detection, a proper understanding of genetic methylation patterns has been used to detect other conditions. The initiation and the maintenance of the inactive X-chromosome in female eutherians were found to depend on methylation (Goto and Monk (1998) Regulation of X-chromosome inactivation in development in mice and humans. Microbiol Mol Biol Rev, 62, 362-378, which is incorporated herein by reference). Rett syndrome (RTT) is an X-linked dominant disease caused by mutation of MeCP2 gene, which is further complicated by X-chromosome inactivation (XCI) pattern. A current model predicts that MeCP2 represses transcription by binding methylated CpG residues and mediating chromatin remodeling (Dragich et al. (2000) Rett syndrome: a surprising result of mutation in MECP2. Hum Mol Genet, 9, 2365-2375, which is incorporated herein by reference).

Several technical challenges hinder development of methylation detection techniques into a robust and cost efficient screening tool. For example, the accuracy and affordability of currently available techniques can be compromised by impurities in samples that are to be tested. As a result, cumbersome and expensive purification techniques are often employed to purify a genomic sample from background nucleic acids. For example, tumor biopsy techniques are employed to physically separate tumor tissues from healthy tissues. Depending upon the depth of the tissue in the body of an individual, biopsy can require unpleasant and risky harvesting procedures such as needle biopsy, endoscopy, bronchoscopy, colonoscopy or surgery. The presence of circulating tumor DNA in blood provides an attractive alternative to such biopsy techniques. However, circulating tumor DNA is typically present in low quantities and in a background of a relatively large quantity of non-tumor DNA.

Thus there is a need for methods to distinguish methylation patterns in complex genomic samples from particular tissues of interest (e.g. tumor DNA), often in a background of other genomic material from other tissues (e.g. circulating DNA). The methods and apparatus set forth herein satisfy this need and provide other advantages as well.

BRIEF SUMMARY

The present disclosure provides a method for distinguishing an aberrant methylation level for DNA from a first cell type. The method can include steps of (a) providing a test data set that includes (i) methylation states for a plurality of sites from test genomic DNA from at least one test organism, and (ii) coverage at each of the sites for detection of the methylation states; (b) providing methylation states for the plurality of sites in reference genomic DNA from one or more reference individual organisms, (c) determining, for each of the sites, the methylation difference between the test genomic DNA and the reference genomic DNA, thereby providing a normalized methylation difference for each site; and (d) weighting the normalized methylation difference for each site by the coverage at each of the sites, thereby determining an aggregate coverage-weighted normalized methylation difference score.

Also provided is a method for distinguishing an aberrant methylation level for DNA from a sample containing DNA from a plurality of different cell types, including steps of (a) providing a sample containing a mixture of genomic DNA from a plurality of different cell types from at least one test organism, thereby providing test genomic DNA; (b) detecting methylation states for a plurality of sites in the test genomic DNA; (c) determining the coverage at each of the sites for the detecting of the methylation states; (d) providing methylation states for the plurality of sites in reference genomic DNA from at least one reference individual, the at least one test organism and reference individual optionally being the same species; (e) determining, for each of the sites, the methylation difference between the test genomic DNA and the reference genomic DNA, thereby providing a normalized methylation difference for each site; and (f) weighting the normalized methylation difference for each site by the coverage at each of the sites, thereby determining an aggregate coverage-weighted normalized methylation difference score.

In particular embodiments, this disclosure provides a method for detecting a condition such as cancer. The method can include steps of (a) providing a mixture of genomic DNA from blood of an individual suspected of having the condition (e.g. cancer), wherein the mixture comprises genomic DNA from a plurality of different cell types from the individual, thereby providing test genomic DNA; (b) detecting methylation states for a plurality of sites in the test genomic DNA; (c) determining the coverage at each of the sites for the detecting of the methylation states; (d) providing methylation states for the plurality of sites in reference genomic DNA from at least one reference individual, the reference individual being known to have the condition (e.g. cancer) or known to not have the condition (e.g. cancer); (e) determining, for each of the sites, the methylation difference between the test genomic DNA and the reference genomic DNA, thereby providing a normalized methylation difference for each site; (f) weighting the normalized methylation difference for each site by the coverage at each of the sites, thereby determining an aggregate coverage-weighted normalized methylation difference score; and (g) determining that the individual does or does not have the condition (e.g. cancer) based on the aggregate coverage-weighted normalized methylation difference score.

The present disclosure also provides an alternative sensitive method for distinguishing an aberrant methylation level for DNA from a first cell type. The method can include a first stage of establishing a methylation baseline, including the steps of (a) providing methylation states for a plurality of sites in baseline genomic DNA from two or more normal individual organisms; and (b) determining, for each of the sites, the mean methylation level and standard deviation of methylation levels for the baseline genomic DNA; a second stage of determining aggregate methylation scores for a plurality of training samples, including the steps of (c) providing a training set of normal genomic DNA samples from two or more normal individual organisms that includes (i) methylation states for a plurality of sites in the training set of normal genomic DNA samples, and optionally (ii) coverage at each of the sites for detection of the methylation states; (d) determining, for each of the sites, the methylation difference between each normal genomic DNA sample of the training set and the baseline genomic DNA, thereby providing a normalized methylation difference for each normal genomic DNA sample of the training set at each site; (e) converting the normalized methylation difference for each normal genomic DNA sample of the training set at each site into the probability of observing such a normalized methylation difference or greater, and optionally weighting the probability of such an event; (f) determining an aggregate methylation score for each normal genomic DNA sample of the training set to obtain training set methylation scores; and (g) calculating the mean methylation score and standard deviation of the training set methylation scores; a third stage, which can be carried out before, after, or concurrently with the second stage, of determining an aggregate methylation score for a given test sample, including the steps of (h) providing a test data set that includes (i) methylation states for the plurality of sites from test genomic DNA from at least one test organism, and optionally (ii) coverage at each of the sites for detection of the methylation states; (i) determining, for each of the sites, the methylation difference between the test genomic DNA and the baseline genomic DNA, thereby providing a normalized methylation difference for the test genomic DNA; (j) converting the normalized methylation difference for the test genomic DNA at each of the sites into the probability of observing such a normalized methylation difference or greater, and optionally weighting the probability of such an event; and (k) determining an aggregate methylation score for the test genomic DNA; and a fourth stage of (1) comparing the methylation score of the test genomic DNA to the mean methylation score and standard deviation of methylation scores in the training set of normal genomic DNA to determine the number of standard deviations the methylation score of the test genomic DNA is from the distribution of methylation scores in the training set of normal genomic DNA.

Also provided is an alternative sensitive method for distinguishing an aberrant methylation level for DNA from a sample containing DNA from a plurality of different cell types. The method can include a first stage of establishing a methylation baseline, including the steps of (a) providing methylation states for a plurality of sites in baseline genomic DNA from two or more normal individual organisms; and (b) determining, for each of the sites, the mean methylation level and standard deviation of methylation levels for the baseline genomic DNA; a second stage of determining aggregate methylation scores for a plurality of training samples, including the steps of (c) providing a training set of normal genomic DNA samples from two or more normal individual organisms that includes (i) methylation states for a plurality of sites in the training set of normal genomic DNA samples, and optionally (ii) coverage at each of the sites for detection of the methylation states; (d) determining, for each of the sites, the methylation difference between each normal genomic DNA sample of the training set and the baseline genomic DNA, thereby providing a normalized methylation difference for each normal genomic DNA sample of the training set at each site; (e) converting the normalized methylation difference for each normal genomic DNA sample of the training set at each site into the probability of observing such a normalized methylation difference or greater, and optionally weighting the probability; (f) determining an aggregate methylation score for each normal genomic DNA sample of the training set to obtain training set methylation scores; and (g) calculating the mean methylation score and standard deviation of the training set methylation scores; a third stage, which can be carried out before, after, or concurrently with the second stage, of determining an aggregate methylation score for a given test sample, including the steps of (h) providing a mixture of genomic DNA from a test organism suspected of having a condition associated with an aberrant DNA methylation level, wherein the mixture includes genomic DNA from a plurality of different cell types from the test organism, thereby providing test genomic DNA; (i) detecting methylation states for the plurality of sites in the test genomic DNA, and optionally determining the coverage at each of the sites for the detecting of the methylation states; (j) determining, for each of the sites, the methylation difference between the test genomic DNA and the baseline genomic DNA, thereby providing a normalized methylation difference for the test genomic DNA; (k) converting the normalized methylation difference for the test genomic DNA at each of the sites into the probability of observing such a normalized methylation difference or greater, and optionally weighting the probability of such an event; and (1) determining an aggregate methylation score for the test genomic DNA; and a fourth stage of (m) comparing the methylation score of the test genomic DNA to the mean methylation score and standard deviation of methylation scores in the training set of normal genomic DNA to determine the number of standard deviations the methylation score of the test genomic DNA is from the distribution of methylation scores in the training set of normal genomic DNA.

In particular embodiments, this disclosure provides a method for detecting a condition such as cancer. The method can include a first stage of establishing a methylation baseline, including the steps of (a) providing methylation states for a plurality of sites in baseline genomic DNA from at least one normal individual organism; and (b) determining, for each of the sites, the mean methylation level and standard deviation of methylation levels for the baseline genomic DNA; a second stage of determining aggregate methylation scores for a plurality of training samples, including the steps of (c) providing a training set of normal genomic DNA samples from two or more normal individual organisms that includes (i) methylation states for a plurality of sites in the training set of normal genomic DNA samples, and optionally (ii) coverage at each of the sites for detection of the methylation states; (d) determining, for each of the sites, the methylation difference between each normal genomic DNA sample of the training set and the baseline genomic DNA, thereby providing a normalized methylation difference for each normal genomic DNA sample of the training set at each site; (e) converting the normalized methylation difference for each normal genomic DNA sample of the training set at each site into the probability of observing such a normalized methylation difference or greater, and optionally weighting the probability of such an event; (f) determining a methylation score for each normal genomic DNA sample of the training set to obtain training set methylation scores; and (g) calculating the mean methylation score and standard deviation of the training set methylation scores; a third stage, which can be carried out before, after, or concurrently with the second stage, of determining an aggregate methylation score for a given test sample, including the steps of (h) providing a mixture of genomic DNA from a test organism suspected of having the condition, wherein the mixture comprises genomic DNA from a plurality of different cell types from the test organism, thereby providing test genomic DNA; (i) detecting methylation states for the plurality of sites in the test genomic DNA, and optionally determining the coverage at each of the sites for the detecting of the methylation states; (j) determining, for each of the sites, the methylation difference between the test genomic DNA and the baseline genomic DNA, thereby providing a normalized methylation difference for the test genomic DNA; (k) converting the normalized methylation difference for the test genomic DNA at each of the sites into the probability of observing such a normalized methylation difference or greater, and optionally weighting the probability of such an event; and (l) determining a methylation score for the test genomic DNA; and a fourth stage of (m) comparing the methylation score of the test genomic DNA to the mean methylation score and standard deviation of methylation scores in the training set of normal genomic DNA to determine the number of standard deviations the methylation score of the test genomic DNA is from the distribution of methylation scores in the training set of normal genomic DNA.

The present disclosure provides a method for using methylation levels to identify or classify a specific type of cancer in a test organism. The method can include a first stage of identifying specific cancers that can be used as a cancer type, including (a) providing a data set that includes methylation states for a plurality of sites from genomic DNA from clinical samples known to include a specific cancer; a second stage of selecting hypermethylated sites that includes (b) identifying hypermethylated sites characteristic of a cancer type, including (i) determining a mean methylation level for each site in the genomic DNA of the clinical samples known to include the specific cancer, (ii) determining which sites meet a first threshold, a second threshold, or a combination thereof, where determining the first threshold includes (1) determining the absolute value of the mean methylation level of each site; (2) ranking the mean methylation levels for each site from lowest to highest, and (3) selecting those sites having a mean methylation level at a percentile rank that is greater than or equivalent to a first preselected value, and where determining the second threshold includes (1) determining the absolute value of the mean methylation level of each site; and (2) selecting those sites having a mean methylation level that is greater than a second preselected value, and (iii) compiling a list of hypermethylated sites that are characteristic for the cancer type; and (c) repeating (a) and (b) for each specific cancer, to result in a plurality of lists of hypermethylated sites that are characteristic for additional cancer types; a third stage that includes analyzing a test genomic DNA sample from a test organism by (d) providing a test data set that includes a methylation level for each hypermethylated site from a test genomic DNA from an individual test organism, wherein the hypermethylated sites are from one of the lists of hypermethylated sites that is characteristic for a cancer type identified in steps (b) and (c); (e) averaging the methylation level of each of the hypermethylated sites to result in a single average methylation level for the test genomic DNA for the cancer type identified in steps (b) and (c); (f) repeating step (e) for each cancer type, to result in an average methylation level for each cancer type; and (g) ranking the average methylation levels for each cancer type from lowest to highest, wherein the cancer type corresponding to the highest average methylation level is the cancer present in the individual test organism.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows criteria for designing probes to regions of a genome having methylation sites.

FIG. 2 shows a workflow for targeted circulating tumor DNA (ctDNA) methylation sequencing.

FIG. 3 shows aggregate coverage-weighted normalized methylation differences (z-scores) determined as described herein for various cancer samples at various titration levels.

FIG. 4 shows coverage-weighted methylation scores determined as described herein for colorectal cancer samples at various titration levels.

FIG. 5 shows methylation scores for 66 samples from advanced cancer patients and 25 samples from normal individuals, demonstrating the ability of the methylation score algorithm to distinguish advanced cancer samples from normal samples.

FIG. 6 shows a tabulated summary of the methylation scores shown in FIG. 5.

FIG. 7 shows correlation of methylation profiles between plasma and tissue DNA samples from cancer patients.

FIG. 8 shows cancer type classification results on tumor tissue samples, demonstrating the ability of the cancer type classification algorithm to identify most tumors based on DNA methylation data with a high degree of accuracy.

FIG. 9 depicts cancer type classification results on plasma DNA samples from cancer patients, showing high clinical sensitivities for colorectal and breast cancers.

DETAILED DESCRIPTION

DNA methylation data can provide valuable information, when evaluated independently or in combination with other information such as genotype or gene expression patterns. One object of the methods set forth herein is to determine this information, e.g. if one or more sites in a genome are differentially methylated in a test sample compared to a reference sample or data set.

Particular embodiments can be used for the detection, screening, monitoring (e.g. for relapse, remission, or response to treatment), staging, classification (e.g. for aid in choosing the most appropriate treatment modality) and prognostication of cancer using methylation analysis of circulating plasma/serum DNA.

Cancer DNA is known to demonstrate aberrant DNA methylation (see, for example, Herman et al. 2003 N Engl J Med 349: 2042-2054, which is incorporated herein by reference). For example, the CpG site promoters of genes, e.g. tumor suppressor genes, are hypermethylated while the CpG sites in the gene body are hypomethylated when compared with non-cancer cells. In particular embodiments of the methods set forth herein, a methylation pattern detected from the blood of an individual suspected of having cancer is indicative of the methylation state of potentially cancerous tissues such that the pattern is expected to be different between individuals with cancer when compared with those healthy individuals without cancer or when compared with those whose cancer has been cured.

Because aberrant methylation occurs in most cancers, the methods described herein can be applied to the detection of any of a variety of malignancies with aberrant methylation, for example, malignancies in lung, breast, colorectum, prostate, nasopharynx, stomach, testes, skin, nervous system, bone, ovary, liver, hematologic tissues, pancreas, uterus, kidney, lymphoid tissues, etc. The malignancies may be of a variety of histological subtypes, for example, carcinomas, adenocarcinomas, sarcomas, fibroadenocarcinoma, neuroendocrine, or undifferentiated.

In particular embodiments, a method for determining methylation patterns can be used to monitor development of a fetus (e.g. to determine the presence or absence of a developmental abnormality) or to determine the presence of a particular disease or condition. In such cases the method can be carried out using a sample (e.g. blood, tissue or amniotic fluid) obtained from a pregnant female and the sample can be evaluated for methylation levels of fetal nucleic acids. A DNA methylation profile of placental tissues can be used to evaluate the pathophysiology of pregnancy-associated or developmentally-related diseases, such as preeclampsia and intrauterine growth restriction. Disorders in genomic imprinting are associated with developmental disorders, such as Prader-Willi syndrome and Angelman syndrome, and can be identified or evaluated using methods of the present disclosure. Altered profiles of genomic imprinting and global DNA methylation in placental and fetal tissues have been observed in pregnancies resulting from assisted reproductive techniques (see, for example, Hiura et al. 2012 Hum Reprod; 27: 2541-2548, incorporated herein by reference) and can be detected using methods set forth herein. Exemplary methods that can be modified for use with the methods of the present disclosure are forth in US Pat. App. Pub. Nos. 2013/0189684 A1 or 2014/0080715 A1, each of which is incorporated herein by reference.

The ability to determine placental or fetal methylation patterns from maternal plasma provides a noninvasive method to determine, detect and monitor pregnancy-associated conditions such as preeclampsia, intrauterine growth restriction, preterm labor and others. For example, the detection of a disease-specific aberrant methylation signature allows the screening, diagnosis and monitoring of such pregnancy-associated conditions.

Additionally, a method set forth herein to obtain diagnostic or prognostic information for other conditions. For example, liver tissue can be analyzed to determine a methylation pattern specific to the liver, which may be used to identify liver pathologies. Other tissues which can also be analyzed include brain cells, bones, the lungs, the heart, the muscles and the kidneys, etc. DNA can be obtained from blood samples and analyzed in a method set forth herein in order to determine the state of any of a variety of tissues that contribute DNA to the blood.

Furthermore, methylation patterns of transplanted organs can be determined from plasma DNA of organ transplantation recipients. Transplant analysis from plasma, can be a synergistic technology to transplant genomic analysis from plasma, such as technology set forth in Zheng at al. 2012 Clin Chem 58: 549-558; Lo at al. 1998 Lancet 351: 1329-1330; or Snyder et al. 2011 Proc Natl Acad Sci USA; 108: 6229-6234, each of which is incorporated herein by reference.

The methylation patterns of various tissues may change from time to time, e.g. as a result of development, aging, disease progression (e.g. inflammation, cancer or cirrhosis) or treatment. The dynamic nature of DNA methylation makes such analysis potentially very valuable for monitoring of physiological and pathological processes. For example, if one detects a change in the plasma methylation pattern of an individual compared to a baseline value obtained when they were healthy, one could then detect disease processes in organs that contribute plasma DNA.

Terms used herein will be understood to take on their ordinary meaning in the relevant art unless specified otherwise. Several terms used herein and their meanings are set forth below.

As used herein, the term “cell-free,” when used in reference to DNA, is intended to mean DNA that has been removed from a cell in vivo. The removal of the DNA can be a natural process such as necrosis or apoptosis. Cell-free DNA is generally obtained from blood, or a fraction thereof, such as plasma. Cell-free DNA can be obtained from other bodily fluids or tissues.

As used herein, the term “cell type” is intended to identify cells based on morphology, phenotype, developmental origin or other known or recognizable distinguishing cellular characteristic. A variety of different cell types can be obtained from a single organism (or from the same species of organism). Exemplary cell types include, but are not limited to urinary bladder, pancreatic epithelial, pancreatic alpha, pancreatic beta, pancreatic endothelial, bone marrow lymphoblast, bone marrow B lymphoblast, bone marrow macrophage, bone marrow erythroblast, bone marrow dendritic, bone marrow adipocyte, bone marrow osteocyte, bone marrow chondrocyte, promyeloblast, bone marrow megakaryoblast, bladder, brain B lymphocyte, brain glial, neuron, brain astrocyte, neuroectoderm, brain macrophage, brain microglia, brain epithelial, cortical neuron, brain fibroblast, breast epithelial, colon epithelial, colon B lymphocyte, mammary epithelial, mammary myoepithelial, mammary fibroblast, colon enterocyte, cervix epithelial, ovary epithelial, ovary fibroblast, breast duct epithelial, tongue epithelial, tonsil dendritic, tonsil B lymphocyte, peripheral blood lymphoblast, peripheral blood T lymphoblast, peripheral blood cutaneous T lymphocyte, peripheral blood natural killer, peripheral blood B lymphoblast, peripheral blood monocyte, peripheral blood myeloblast, peripheral blood monoblast, peripheral blood promyeloblast, peripheral blood macrophage, peripheral blood basophil, liver endothelial, liver mast, liver epithelial, liver B lymphocyte, spleen endothelial, spleen epithelial, spleen B lymphocyte, liver hepatocyte, liver Alexander, liver fibroblast, lung epithelial, bronchus epithelial, lung fibroblast, lung B lymphocyte, lung Schwann, lung squamous, lung macrophage, lung osteoblast, neuroendocrine, lung alveolar, stomach epithelial and stomach fibroblast. In some embodiments, two cells can be considered to be the same type of cell despite one of the cells having been phenotypically or morphologically altered by a condition or disease such as cancer. For purposes of comparison, a first cell that has been altered by a disease or condition can be compared to a second cell based on the known or suspected state of the first cell prior to having been altered. For example, a cancerous pancreatic ductal epithelium cell can be considered to be the same type of cell as a non-cancerous pancreatic ductal epithelium cell.

As used herein, the term “circulating,” when used in reference to DNA, is intended to mean DNA that is or was moving through the circulatory system of an organism, whether in cell-free form or inside circulating cells.

As used herein, the term “coverage,” when used in reference to a genetic locus, is intended to mean the number of detection events (e.g. sequence reads) that align to, or “cover,” the locus. In some embodiments, the term refers to the average number of detection events (e.g. sequence reads) that align to, or “cover,” a plurality of loci. Generally, the coverage level obtained from a sequencing method correlates directly with the degree of confidence in the accuracy of the call (e.g. nucleotide type or methylation state) determined at a particular base position or genetic locus. At higher levels of coverage, a locus is covered by a greater number of aligned sequence reads, so calls can be made with a higher degree of confidence.

As used herein, the term “CpG site” is intended to mean the location in a nucleic acid molecule, or sequence representation of the molecule, where a cytosine nucleotide and guanine nucleotide occur, the 3′ oxygen of the cytosine nucleotide being covalently attached to the 5′ phosphate of the guanine nucleotide. The nucleic acid is typically DNA. The cytosine nucleotide can optionally contain a methyl moiety, hydroxymethyl moiety or hydrogen moiety at position 5 of the pyrimidine ring.

As used herein, the term “derived,” when used in reference to DNA, is intended to refer to the source from which the DNA was obtained or the origin where the DNA was synthesized. In the case of biologically derived DNA, the term can be used to refer to an in vivo source from which the DNA was obtained or the in vivo origin where the DNA was synthesized. Exemplary origins include, but are not limited to, a cell, cell type, tissue, tissue type, organism or species of organism. In the case of synthetically derived DNA, the term can be used to refer to an in vitro source from which the DNA was obtained or the in vitro origin where the DNA was synthesized. A DNA molecule that is derived from a particular source or origin can nonetheless be subsequently copied or amplified. The sequence of the resulting copies or amplicons can be referred to as having been derived from the source or origin.

As used herein, the term “each,” when used in reference to a collection of items, is intended to identify an individual item in the collection but does not necessarily refer to every item in the collection. Exceptions can occur if explicit disclosure or context clearly dictates otherwise.

As used herein, the term “methylation difference” is intended to mean a qualitative or quantitative indicia that two nucleotides or nucleic acids do not have the same methylation state. The methylation difference can be indicated for nucleotides that are at aligned positions on different nucleic acids. In some cases the methylation difference can be a sum or aggregate of a plurality of aligned positions. When two or more nucleic acids are aligned, the methylation difference can be an average across one or more aligned positions.

As used herein, the term “methylation state,” when used in reference to a locus (e.g., a CpG site or polynucleotide segment) across several molecules having that locus, refers to one or more characteristics of the locus relevant to presence or absence of a methyl moiety. Non-limiting examples of such characteristics include whether any of the cytosine (C) bases within a locus are methylated, location of methylated C base(s), percentage of methylated C base(s) at a particular locus, and allelic differences in methylation due to, for example, difference in the origin of alleles. Reference to the methylation state of a particular CpG site in a nucleic acid molecule, is directed to the presence or absence of a methyl moiety at position 5 of the pyrimidine ring of a cytosine. The term can be applied to one or more cytosine nucleotides (or representations thereof e.g. a chemical formula), or to one or more nucleic acid molecules (or representations thereof e.g. a sequence representation). The term can also refer to the relative or absolute amount (e.g., concentration) of methylated C or non-methylated C at a particular locus in a nucleic acid. A methylation state sometimes is hypermethylated and sometimes is hypomethylated. For example, if all or a majority of C bases within a locus are methylated, the methylation state can be referred to as “hypermethylated.” In another example, if all or a majority of C bases within a locus are not methylated, the methylation state may be referred to as “hypomethylated.” Likewise, if all or a majority of C bases within a locus are methylated as compared to reference then the methylation state is considered hypermethylated compared to the reference. Alternatively, if all or a majority of the C bases within a locus are not methylated as compared to a reference then the methylation state is considered hypomethylated compared to the reference.

A “methylation site” is a locus in a nucleic acid where methylation has occurred, or has the possibility of occurring. A methylation site sometimes is a C base, or multiple C bases in a region, and sometimes a methylation site is a CpG site in a locus. Each methylation site in the locus may or may not be methylated. A methylation site can be susceptible to methylation by a naturally occurring event in vivo or by an event that chemically methylates a nucleotide in vitro.

As used herein, the term “mixture,” when used in reference to two or more components, is intended to mean that the two or more components are simultaneously present in a fluid or vessel. The components are typically capable of contacting each other via diffusion or agitation. The components may be separate molecules (e.g. two or more nucleic acid fragments) or the components may be part of a single molecule (e.g. sequence regions on a long nucleic acid molecule).

As used herein, the term “tissue” is intended to mean a collection or aggregation of cells that act together to perform one or more specific functions in an organism. The cells can optionally be morphologically similar. Exemplary tissues include, but are not limited to, eye, muscle, skin, tendon, vein, artery, blood, heart, spleen, lymph node, bone, bone marrow, lung, bronchi, trachea, gut, small intestine, large intestine, colon, rectum, salivary gland, tongue, gall bladder, appendix, liver, pancreas, brain, stomach, skin, kidney, ureter, bladder, urethra, gonad, testicle, ovary, uterus, fallopian tube, thymus, pituitary, thyroid, adrenal, or parathyroid. Tissue can be derived from any of a variety of organs of a human or other body.

The embodiments set forth below and recited in the claims can be understood in view of the above definitions.

The present disclosure provides a method for distinguishing an aberrant methylation level for DNA from a first cell type. The method can include steps of (a) providing a test data set that includes (i) methylation states for a plurality of sites (e.g. CpG sites) from test genomic DNA from at least one test organism, and (ii) coverage at each of the sites (e.g. CpG sites) for detection of the methylation states; (b) providing methylation states for the plurality of sites (e.g. CpG sites) in reference genomic DNA from one or more reference individual organisms, (c) determining, for each of the sites (e.g. CpG sites), the methylation difference between the test genomic DNA and the reference genomic DNA, thereby providing a normalized methylation difference for each sites (e.g. CpG sites); and (d) weighting the normalized methylation difference for each sites (e.g. CpG sites) by the coverage at each of the sites (e.g. CpG sites), thereby determining an aggregate coverage-weighted normalized methylation difference score. Optionally the sites from the test genomic DNA are derived from a plurality of different cell types from the individual test organism and as a further option the cell type from which each of the sites is derived is unknown. In a further optional embodiment, the individual test organism and the one or more reference individual organisms are the same species.

Also provided is a method for distinguishing an aberrant methylation level for DNA from a sample containing DNA from a plurality of different cell types, including steps of (a) providing a sample containing a mixture of genomic DNA from a plurality of different cell types from at least one test organism, thereby providing test genomic DNA; (b) detecting methylation states for a plurality of sites (e.g. CpG sites) in the test genomic DNA; (c) determining the coverage at each of the sites (e.g. CpG sites) for the detecting of the methylation states; (d) providing methylation states for the plurality of sites (e.g. CpG sites) in reference genomic DNA from at least one reference individual, the at least one test organism and reference individual optionally being the same species; (e) determining, for each of the sites (e.g. CpG sites), the methylation difference between the test genomic DNA and the reference genomic DNA, thereby providing a normalized methylation difference for each site (e.g. CpG site); and (f) weighting the normalized methylation difference for each site (e.g. CpG site) by the coverage at each of the sites (e.g. CpG sites), thereby determining an aggregate coverage-weighted normalized methylation difference score.

The present invention also provides an alternative sensitive method for distinguishing an aberrant methylation level for DNA from a first cell type.

The first stage of this method involves establishing a methylation baseline, including the steps of (a) providing methylation states for a plurality of sites (e.g., CpG sites) in baseline genomic DNA from two or more normal individual organisms; and (b) determining, for each of the sites (e.g., CpG sites), the mean methylation level and standard deviation of methylation levels for the baseline genomic DNA. In some embodiments, the number of normal individual organisms providing baseline genomic DNA is at least 3, at least 5, at least 10, at least 20, at least 50, or at least 100.

The second stage of this method involves determining aggregate methylation scores for a plurality of training samples, including the steps of (c) providing a training set of normal genomic DNA samples from two or more normal individual organisms that includes (i) methylation states for a plurality of sites (e.g., CpG sites) in the training set of normal genomic DNA samples, and optionally (ii) coverage at each of the sites (e.g., CpG sites) for detection of the methylation states; (d) determining, for each of the sites (e.g., CpG sites), the methylation difference between each normal genomic DNA sample of the training set and the baseline genomic DNA, thereby providing a normalized methylation difference for each normal genomic DNA sample of the training set at each site (e.g., CpG site); (e) converting the normalized methylation difference for each normal genomic DNA sample of the training set at each site (e.g., CpG site) into the probability of observing such a normalized methylation difference or greater (e.g., a one-sided p-value), and optionally weighting the probability of such an event; (f) determining an aggregate methylation score for each normal genomic DNA sample of the training set to obtain training set methylation scores; and (g) calculating the mean methylation score and standard deviation of the training set methylation scores. In some embodiments, the number of normal individual organisms providing genomic DNA for the training set is at least 3, at least 5, at least 10, at least 20, at least 50, or at least 100.

The third stage of this method, which can be carried out before, after, or concurrently with the second stage, involves determining an aggregate methylation score for a given test sample, including the steps of (h) providing a test data set that includes (i) methylation states for the plurality of sites (e.g., CpG sites) from test genomic DNA from at least one test organism, and optionally (ii) coverage at each of the sites (e.g., CpG sites) for detection of the methylation states; (i) determining, for each of the sites (e.g., CpG sites), the methylation difference between the test genomic DNA and the baseline genomic DNA, thereby providing a normalized methylation difference for the test genomic DNA; (j) converting the normalized methylation difference for the test genomic DNA at each of the sites (e.g., CpG sites) into the probability of observing such a normalized methylation difference or greater (e.g., a one-sided p-value), and optionally weighting the probability of such an event; and (k) determining an aggregate methylation score for the test genomic DNA.

The fourth and final stage of this method involves the step of (1) comparing the methylation score of the test genomic DNA to the mean methylation score and standard deviation of methylation scores in the training set of normal genomic DNA to determine the number of standard deviations the methylation score of the test genomic DNA is from the distribution of methylation scores in the training set of normal genomic DNA. In the event the number of standard deviations exceeds a predetermined threshold value (e.g., 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, etc.), the test sample is considered to have an aberrant DNA methylation level.

Optionally, the methylation sites (e.g., CpG sites) from the test genomic DNA are derived from a plurality of different cell types from the individual test organism, and as a further option, the cell type from which each of the sites (e.g., CpG sites) is derived is unknown. In a further optional embodiment, the individual test organism and the one or more baseline individual organisms, training individual organisms, or a combination thereof are the same species.

Also provided is an alternative sensitive method for distinguishing an aberrant methylation level for DNA from a sample containing DNA from a plurality of different cell types.

The first stage of this method involves establishing a methylation baseline, including the steps of (a) providing methylation states for a plurality of sites (e.g., CpG sites) in baseline genomic DNA from two or more normal individual organisms; and (b) determining, for each of the sites (e.g., CpG sites), the mean methylation level and standard deviation of methylation levels for the baseline genomic DNA. In some embodiments, the number of normal individual organisms providing baseline genomic DNA is at least 3, at least 5, at least 10, at least 20, at least 50, or at least 100.

The second stage of this method involves determining aggregate methylation scores for a plurality of training samples, including the steps of (c) providing a training set of normal genomic DNA samples from two or more normal individual organisms that includes (i) methylation states for a plurality of sites (e.g., CpG sites) in the training set of normal genomic DNA samples, and optionally (ii) coverage at each of the sites (e.g., CpG sites) for detection of the methylation states; (d) determining, for each of the sites (e.g., CpG sites), the methylation difference between each normal genomic DNA sample of the training set and the baseline genomic DNA, thereby providing a normalized methylation difference for each normal genomic DNA sample of the training set at each site (e.g., CpG site); (e) converting the normalized methylation difference for each normal genomic DNA sample of the training set at each site (e.g., CpG sites) into the probability of observing such a normalized methylation difference or greater (e.g., a one-sided p-value), and optionally weighting the probability; (f) determining an aggregate methylation score for each normal genomic DNA sample of the training set to obtain training set methylation scores; and (g) calculating the mean methylation score and standard deviation of the training set methylation scores. In some embodiments, the number of normal individual organisms providing genomic DNA for the training set is at least 3, at least 5, at least 10, at least 20, at least 50, or at least 100.

The third stage of this method, which can be carried out before, after, or concurrently with the second stage, involves determining an aggregate methylation score for a given test sample, including the steps of (h) providing a mixture of genomic DNA from a test organism suspected of having a condition associated with an aberrant DNA methylation level (e.g., cancer), wherein the mixture comprises genomic DNA from a plurality of different cell types from the test organism, thereby providing test genomic DNA; (i) detecting methylation states for the plurality of sites (e.g., CpG sites) in the test genomic DNA, and optionally determining the coverage at each of the sites (e.g., CpG sites) for the detecting of the methylation states; (j) determining, for each of the sites (e.g., CpG sites), the methylation difference between the test genomic DNA and the baseline genomic DNA, thereby providing a normalized methylation difference for the test genomic DNA; (k) converting the normalized methylation difference for the test genomic DNA at each of the sites (e.g., CpG sites) into the probability of observing such a normalized methylation difference or greater (e.g., a one-sided p-value), and optionally weighting the probability of such an event; and (1) determining an aggregate methylation score for the test genomic DNA.

The fourth and final stage of this method involves the step of (m) comparing the methylation score of the test genomic DNA to the mean methylation score and standard deviation of methylation scores in the training set of normal genomic DNA to determine the number of standard deviations the methylation score of the test genomic DNA is from the distribution of methylation scores in the training set of normal genomic DNA. In the event the number of standard deviations exceeds a predetermined threshold value (e.g., 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, etc.), the test sample is considered to have an aberrant DNA methylation level.

A method set forth herein can be carried out for any of a variety of test organisms. Exemplary organisms include, without limitation, eukaryotic (unicellular or multicellular) organisms. Exemplary eukaryotic organisms include a mammal such as a rodent, mouse, rat, rabbit, guinea pig, ungulate, horse, sheep, pig, goat, cow, cat, dog, primate, human or non-human primate; a plant such as Arabidopsis thaliana, corn (Zea mays), sorghum, oat (Oryza sativa), wheat, rice, canola, or soybean; an algae such as Chlamvdomonas reinhardtii; a nematode such as Caenorhabditis elegans; an insect such as Drosophila melanogaster, mosquito, fruit fly, honey bee or spider; a fish such as zebrafish (Danio rerio); a reptile: an amphibian such as a frog or Xenopus laevis; a Dictyostelium discoideum; a fungi such as Pneumocystis carinii, Takifugu rubripes, yeast such as Saccharamoyces cerevisiae or Schizosaccharomyces pombe; or a Plasmodium falciparum. A method of the present disclosure can also be used to evaluate methylation in organisms such as prokaryotes, examples of which include a bacterium, Escherichia coli, Staphylococci or Mycoplasma pneumoniae; an archae; a virus, examples of which include Hepatitis C virus or human immunodeficiency virus; or a viroid.

Particular embodiments of the methods set forth herein can provide advantages when applied to multicellular organisms because the methods provide for determination of the methylation states for genomic DNA derived from a particular cell or tissue in a background of nucleic acids derived from other cells or tissues. Thus, the methods set forth herein can be particularly useful for mammals, such as humans. In some cases the methods can be carried out on samples containing nucleic acid mixtures from several different cell types or tissue types such as samples obtained from the blood or other biological fluid of a multicellular organism. Furthermore, the methods set forth herein can be advantageously employed for evaluation of methylation states for genomic DNA obtained from somatic cells of a pregnant female mammal, such as a pregnant female human, and/or the methylation states for genomic DNA obtained from somatic cells of one or more prenatal offspring carried by the female.

In some embodiments, the methods can be carried out for a mixture of genomic DNA from several different cell types from a mixed organism environment (e.g. metagenomics sample) such as an ecological sample (e.g. pond, ocean, thermal vent, etc.) or digestive system sample (e.g. mouth, gut, colon, etc.). Thus, the method can be carried out for a mixed organism sample wherein individual species are not separated or cultivated.

As will be evident from several exemplary embodiments set forth herein, the CpG sites from a test genomic DNA that are evaluated in a method of this disclosure can optionally be derived from a plurality of different cell types from the individual test organism. As a further option the cell type from which each of the CpG sites is derived need not be known. This will often be the case when the sample used in the method is derived from blood or another biological fluid or metagenomics sample.

In particular embodiments, the test sample used in a method set forth herein can include circulating tumor DNA and circulating non-tumor DNA. This can be the case when the test sample includes DNA obtained from blood, for example, from an individual known or suspected to have cancer.

Particular embodiments of the methods set forth herein can be carried out using methylation states for a plurality of sites from test genomic DNA from an individual test organism. In some cases the data is provided to an individual or system that carries out the method. Alternatively, embodiments of the methods can include one or more steps for detecting methylation states for a plurality of sites in a test genome.

Methylation of sites, such as CpG dinucleotide sequences, can be measured using any of a variety of techniques used in the art for the analysis of such sites. For example, methylation can be measured by employing a restriction enzyme based technology, which utilizes methylation sensitive restriction endonucleases for the differentiation between methylated and unmethylated cytosines. Restriction enzyme based technologies include, for example, restriction digest with methylation-sensitive restriction enzymes followed by nucleic acid sequencing (e.g. massively parallel or Next Generation sequencing), Southern blot analysis, real time PCR, restriction landmark genomic scanning (RLGS) or differential methylation hybridization (DMH).

Restriction enzymes characteristically hydrolyze DNA at and/or upon recognition of specific sequences or recognition motifs that are typically between 4- to 8-bases in length. Among such enzymes, methylation sensitive restriction enzymes are distinguished by the fact that they either cleave, or fail to cleave DNA according to the cytosine methylation state present in the recognition motif, in particular, of the CpG sequences. In methods employing such methylation sensitive restriction enzymes, the digested DNA fragments can be differentially separated (e.g. based on size or hybridization affinity to complementary probes), differentially amplified (e.g. based on affinity to an amplification primer), or differentially detected (e.g. via a microarray detection technique or nucleic acid sequencing technique) such that the methylation status of the sequence can thereby be deduced.

In some embodiments that employ methylation sensitive restriction enzymes, a post-digest PCR amplification step is added wherein a set of two oligonucleotide primers, one on each side of the methylation sensitive restriction site, is used to amplify the digested genomic DNA. PCR products are produced and detected for templates that were not restricted (e.g. due to presence of a methylated restriction site) whereas PCR products are not produced where digestion of the subtended methylation sensitive restriction enzyme site occurs. Techniques for restriction enzyme based analysis of genomic methylation are well known in the art and include the following: differential methylation hybridization (DMH) (Huang et al., 1999, Human Mol. Genet. 8, 459-70); Not I-based differential methylation hybridization (for example, WO02/086163A1); restriction landmark genomic scanning (RLGS) (Plass et al., 1999, Genomics 58:254-62); methylation sensitive arbitrarily primed PCR (AP-PCR) (Gonzalgo et al., 1997, Cancer Res. 57: 594-599); methylated CpG site amplification (MCA) (Toyota et. al., 1999, Cancer Res. 59: 2307-2312). Other useful methods for detecting genomic methylation are described, for example, in US Patent Application publication 2003/0170684 A1 or WO 04/05122. The references cited above are incorporated herein by reference.

Methylation of CpG dinucleotide sequences can also be measured by employing cytosine conversion based technologies, which rely on methylation status-dependent chemical modification of CpG sequences within isolated genomic DNA, or fragments thereof, followed by DNA sequence analysis. Chemical reagents that are able to distinguish between methylated and non-methylated CpG dinucleotide sequences include hydrazine, which cleaves the nucleic acid, and bisulfite. Bisulfite treatment followed by alkaline hydrolysis specifically converts non-methylated cytosine to uracil, leaving 5-methylcytosine unmodified as described by Olek A., 1996, Nucleic Acids Res. 24:5064-6 or Frommer et al., 1992, Proc. Natl. Acad. Sci. USA 89:1827-1831, each of which is incorporated herein by reference. The bisulfite-treated DNA can subsequently be analyzed by molecular techniques, such as PCR amplification, sequencing, and detection comprising oligonucleotide hybridization (e.g. using nucleic acid microarrays).

Techniques for the analysis of bisulfite treated DNA can employ methylation-sensitive primers for the analysis of CpG methylation status with isolated genomic DNA, for example, as described by Herman et al., 1996, Proc. Natl. Acad. Sci. USA 93:9821-9826, or U.S. Pat. Nos. 5,786,146 or 6,265,171, each of which is incorporated herein by reference. Methylation sensitive PCR (MSP) allows for the detection of a specific methylated CpG position within, for example, the regulatory region of a gene. The DNA of interest is treated such that methylated and non-methylated cytosines are differentially modified, for example, by bisulfite treatment, in a manner discernable by their hybridization behavior. PCR primers specific to each of the methylated and non-methylated states of the DNA are used in PCR amplification. Products of the amplification reaction are then detected, allowing for the deduction of the methylation status of the CpG position within the genomic DNA. Other methods for the analysis of bisulfite treated DNA include methylation-sensitive single nucleotide primer extension (Ms-SNuPE) (see, for example, Gonzalgo & Jones, 1997; Nucleic Acids Res. 25:2529-2531, or U.S. Pat. No. 6,251,594, each of which is incorporated herein by reference), or the use of real time PCR based methods, such as the art-recognized fluorescence-based real-time PCR technique MethyLight™ (see, for example, Eads et al., 1999; Cancer Res. 59:2302-2306, U.S. Pat. No. 6,331,393 or Heid et al., 1996, Genome Res. 6:986-994, each of which is incorporated herein by reference). It will be understood that a variety of methylation assay methods can be used for the determination of the methylation status of particular genomic CpG positions. Methods which employ bisulfite conversion include, for example, bisulfite sequencing, methylation-specific PCR, methylation-sensitive single nucleotide primer extension (Ms-SnuPE), MALDI mass spectrometry and methylation-specific oligonucleotide arrays, for example, as described in U.S. Pat. No. 7,611,869 or International Patent Application WO2004/051224, each of which is incorporated herein by reference.

In particular embodiments, methylation of genomic CpG positions in a sample can be detected using an array of probes. In such embodiments, a plurality of different probe molecules can be attached to a substrate or otherwise spatially distinguished in an array. Exemplary arrays that can be used in the invention include, without limitation, slide arrays, silicon wafer arrays, liquid arrays, bead-based arrays and others known in the art or set forth in further detail herein. In preferred embodiments, the methods of the invention can be practiced with array technology that combines a miniaturized array platform, a high level of assay multiplexing, and scalable automation for sample handling and data processing. Particularly useful arrays are described in U.S. Pat. Nos. 6,355,431; 6,429,027; 6,890,741; 6,913,884 or 7,582,420; or U.S. Pat. App. Pub. Nos. 2002/0102578 A1; 2005/0053980 A1; 2005/0181440 A1; or 2009/0186349 A1, each of which is incorporated herein by reference. Further examples of useful arrays include those described in U.S. Pat. Nos. 6,023,540, 6,200,737 or 6,327,410; or PCT Pub. Nos. WO9840726, WO9918434 or WO9850782, each of which is incorporated herein by reference.

The plexity of an array used in the invention can vary depending on the probe composition and desired use of the array. For example, the plexity of nucleic acids (or CpG sites) detected in an array can be at least 10, 100, 1,000, 10,000, 0.1 million, 1 million, 10 million, 100 million or more. Alternatively or additionally, the plexity can be selected to be no more than 100 million, 10 million, 1 million, 0.1 million, 10,000, 1,000, 100 or less. Of course, the plexity can be between one of the lower values and one of the upper values selected from the ranges above. Similar plexitiy ranges can be achieved using nucleic acid sequencing approaches such as those known in the art as Next Generation or massively parallel sequencing.

A variety of commercially available array-based products for detection of methylation can be used including, for example, the MethylationEPIC™ BeadChip™(Illumina, Inc., San Diego, Calif.) which allows interrogation of over 850,000 methylation sites quantitatively across the human genome at single-nucleotide resolution. Also useful are methylation microarrays available from Agilent (Santa Clara, Calif.) and other commercial suppliers of nucleic acid arrays. The array products can be customized for detection of a wide variety of methylation sites in the human genome or other genomes.

Detection of one or more nucleic acids obtained or generated in a technique set forth herein can employ a sequencing procedure, such as a sequencing-by-synthesis (SBS) technique or other techniques known in the art as massively parallel sequencing or Next Generation sequencing. Briefly, SBS can be initiated by contacting the target nucleic acids with one or more labeled nucleotides, DNA polymerase, etc. The target nucleic acid can be derived from a methylation detection technique such as bisulfate conversion or restriction with a methyl sensitive restriction endonuclease. Those features where a primer is extended using the target nucleic acid as template will incorporate a labeled nucleotide that can be detected. Optionally, the labeled nucleotides can further include a reversible termination property that terminates further primer extension once a nucleotide has been added to a primer. For example, a nucleotide analog having a reversible terminator moiety can be added to a primer such that subsequent extension cannot occur until a deblocking agent is delivered to remove the moiety. Thus, for embodiments that use reversible termination, a deblocking reagent can be delivered to the flow cell (before or after detection occurs). Washes can be carried out between the various delivery steps. The cycle can then be repeated n times to extend the primer by n nucleotides, thereby detecting a sequence of length n. Exemplary SBS procedures, fluidic systems and detection platforms that can be readily adapted for use with a method of the present disclosure are described, for example, in Bentley et al., Nature 456:53-59 (2008), WO 04/018497; WO 91/06678; WO 07/123744; U.S. Pat. Nos. 7,057,026; 7,329,492; 7,211,414; 7,315,019 or 7,405,281, or US Pat. App. Pub. No. 2008/0108082 A1, each of which is incorporated herein by reference.

Other sequencing procedures that detect large numbers of nucleic acids in parallel can be used, such as pyrosequencing. Pyrosequencing detects the release of inorganic pyrophosphate (PPi) as particular nucleotides are incorporated into a nascent nucleic acid strand (Ronaghi, et al., Analytical Biochemistry 242(1), 84-9 (1996); Ronaghi, Genome Res. 11(1), 3-11 (2001); Ronaghi et al. Science 281(5375), 363 (1998); or U.S. Pat. Nos. 6,210,891; 6,258,568 or 6,274,320, each of which is incorporated herein by reference). Sequencing-by-ligation reactions are also useful including, for example, those described in Shendure et al. Science 309:1728-1732 (2005); or U.S. Pat. Nos. 5,599,675 or 5,750,341, each of which is incorporated herein by reference. Some embodiments can include sequencing-by-hybridization procedures as described, for example, in Bains et al., Journal of Theoretical Biology 135(3), 303-7 (1988); Drmanac et al., Nature Biotechnology 16, 54-58 (1998); Fodor et al., Science 251(4995), 767-773 (1995); or WO 1989/10977, each of which is incorporated herein by reference. Techniques that use fluorescence resonance energy transfer (FRET) and/or zeromode waveguides can be used such as those described in Levene et al. Science 299, 682-686 (2003); Lundquist et al. Opt. Lett. 33, 1026-1028 (2008); or Korlach et al. Proc. Natl. Acad. Sci. USA 105, 1176-1181 (2008), the disclosures of which are incorporated herein by reference. Also useful are sequencing techniques that employ detection of a proton released upon incorporation of a nucleotide into an extension product, such as those commercially available from Ion Torrent (Guilford, Conn., a Life Technologies subsidiary) or described in US Pat. App. Pub. Nos. 2009/0026082 A1; 2009/0127589 A1; 2010/0137143 A1; or 2010/0282617 A1, each of which is incorporated herein by reference.

Particularly useful sequencing platforms that can be employed include those commercially available from Illumina, Inc. (San Diego, Calif.) such as the MiSeq™, NextSeq™ or HiSeq™ lines of nucleic acid sequencers; the 454 sequencing systems commercially available from Roche Life Sciences (Basel, Switzerland); the Ion Torrent sequencing systems available from Life Technologies, a subsidiary of Thermo Fisher Scientific (Waltham, Mass.); or the nanopore sequencing systems commercially available from Oxford Nanopore (Oxford, England). The TruSeq™ DNA Methylation Kit is available from Illumina, Inc. and can be used to produce bisulfite sequencing libraries that can be detected on Illumina sequencers. Useful commercial products for preparing nucleic acid samples for detection of methylation on sequencing platforms from Illumina or other suppliers include, for example, Methylation Analysis Sample Prep Products available from Thermo Fisher Scientific (Waltham, Mass.), Accel-NGS® Methyl-Seq DNA Library Kit (Swift Biosciences, Ann Arbor, Mich.), EpiMark® Methylated DNA Enrichment Kit available from New England BioLabs (Beverley, Mass.), the Pico Methyl-Seg™ Library Prep Kit available from Zymoresearch (Irvine, Calif.), or the Methylamp™ Universal Methylated DNA Preparation Kit available from EpiGentek (Farmingdale, N.Y.).

Particular embodiments can include a step of manipulating a nucleic acid sample to enrich for desired nucleic acids. For example, a sample that is provided for use in a method set forth herein can be subjected to targeted selection of a subset of genomic DNA fragments that include a set of predetermined target CpG sites. Targeted selection can occur prior to or after treating nucleic acids with bisulfite, methyl sensitive endonucleases or other reagents used to distinguish methylated sites from unmethylated sites. A useful targeted selection technique is set forth in Example I, below.

Particular embodiments of the methods set forth herein will evaluate and/or use the coverage determined for each of the sites where methylation states have been or will be determined. In some cases the coverage data is provided to an individual or system that carries out the method. Alternatively, embodiments of the methods can include one or more steps for determining coverage at each of the sites.

For embodiments that detect methylation states via a sequencing technique, coverage can be considered to describe the average number of sequencing reads that align to, or “cover,” particular sites (e.g. CpG sites). The Next Generation sequencing coverage level often determines whether a particular sequence or site can be characterized with a certain degree of confidence. At higher levels of coverage, each site is covered by a greater number of aligned sequence reads, so characterizations can be made with a higher degree of confidence. A useful guide for determining coverage is provided by Illumina Technical Note “Estimating Sequencing Coverage” Pub. No. 770-2011-022 (Dec. 1, 2014), which is incorporated herein by reference. Similar coverage criteria can be applied to other detection techniques besides Next Generation sequencing techniques.

Particular embodiments of the present invention can use coverage that is at least 10×, 30×, 50×, 100×, 1,000×, 5,000×, 10,000× or more at each site. Alternatively or additionally, coverage can be at most 10,000×, 5,000×, 1,000×, 100×, 50×, 30×, 10× or less. Coverage can be selected based on a desired confidence in determining methylation pattern taken in view of the number of sites being evaluated and the quantity of DNA used in the method.

As the number of sites evaluated increases, the confidence in the characterization of the sites will also increase. This means a lower coverage can be acceptable. In particular embodiments the number of sites evaluated can be at least 10 sites, 100 sites, 500 sites, 1×10³ sites, 5×10³ sites, 1×10⁴ sites, 1×10⁵ sites, 1×10⁶ sites or more. Alternatively or additionally, the number of sites evaluated can be at most 1×10⁶ sites, 1×10⁵ sites, 1×10⁴ sites, 1×10³ sites, 100 sites or 10 sites.

The quantity of DNA used in a method set forth herein will depend upon several factors such as the sample used and the analytical steps carried out on the sample. A typical blood draw will provide 30 ng of circulating DNA. However, larger or smaller quantities of DNA can be provided by altering the volume of blood drawn, by using a different type of sample (such as those exemplified elsewhere herein) and/or utilizing sample extraction techniques with higher or lower yields. Accordingly, a method of the present invention can be carried out using a quantity of DNA that is at least 3 ng, 10 ng, 30 ng, 50 ng, 100 ng, 500 ng or more. Alternatively or additionally, the quantity of DNA can be at most 500 ng, 100 ng, 50 ng, 30 ng, 10 ng or 3 ng.

Furthermore, in some embodiments the DNA used in a method for evaluating methylation states is a mixture of DNA from a target cell or tissue (e.g. tumor DNA) in a background of DNA from other cells or tissues (e.g. non-tumor DNA). The percent DNA from the target tissue or cell can be at most 90%, 50%, 25%, 10%, 1%, 0.1%, 0.01% or lower. Alternatively or additionally, the percent DNA from the target tissue or cell can be at least 0.01%, 0.1%, 1%, 10%, 25%, 50%, 90% or higher.

The above parameters of DNA amount, coverage, number of sites and percent DNA from the target cell or tissue can be adjusted, for example, within the ranges exemplified above to accommodate a desired confidence level in characterizing methylation states for nucleic acids in a method set forth herein.

Particular embodiments of the methods set forth herein include a step of providing methylation states for the plurality of sites in reference genomic DNA from one or more reference individual organisms. Optionally, a method can include one or more steps for detecting the methylation states for the plurality of sites in reference genomic DNA from one or more reference individual organisms. In one aspect, a reference genomic DNA can include, for instance, baseline samples. Any one of the methods set forth herein for determining methylation states of test DNA can be used to determine methylation states for reference DNA.

Reference genomic DNA, such as baseline samples, that is used in a method of the present disclosure can be from one or more organism that is (or are) the same species as the test organism. For example, when the test organism is an individual human, the reference genomic DNA can be from a different human individual. In some embodiments, the reference genomic DNA is from the same individual who provided the test genomic DNA material. For example, the test DNA can be from a tissue suspected of having a particular condition, whereas the reference DNA is from a tissue that is known not to have the condition. In particular embodiments, the test DNA can be from a tumor sample obtained from an individual whereas the reference DNA is from a normal tissue obtained from the same individual. The tissue or cell types can be the same, but for the fact that one of the tissue or cell types has a condition that the other tissue or cell type does not. Alternatively, different tissue or cell types can be obtained from the individual, one of the tissue or cell types providing test DNA and the other tissue or cell type providing reference DNA. A reference genomic DNA can be obtained from a metagenomics sample (e.g. environmental or community sample), for example, to be used in comparison to a test metagenomics sample.

A test DNA can be derived from one or more test organisms at a different time from when a reference DNA, such as baseline samples, is derived from the one or more test organisms. For example, a reference DNA sample can be obtained from an individual at a time prior to when a disease or condition is suspected to be present, and then a test DNA sample can be obtained from the individual at a later time when the individual is suspected of having a disease or condition. In such embodiments the test DNA and reference DNA can be obtained from similar tissues, communities or cell types or from different tissues, communities or cell types.

In one embodiment, a method of the present disclosure can include a step of determining, for a plurality of sites (e.g. CpG sites), the methylation difference between test genomic DNA and reference genomic DNA, thereby providing a normalized methylation difference for each site (e.g. CpG site). In particular embodiments the normalized methylation difference, also referred to as z-score, at a particular site (e.g., CpG site) is determined according to the formula

$Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$

wherein Z_(i) represents a normalized methylation difference for a particular site identified as i, χ_(i) represents the methylation level at site i in the test genomic DNA, μ_(i) represents the mean methylation level at site i in the reference genome, and σ_(i) represents the standard deviation of methylation levels at site i in the reference genomic DNA. Use of the formula for determining methylation difference is exemplified in Example I, below.

A method of the present disclosure can further include a step of weighting the normalized methylation difference for each site (e.g., CpG site) by the coverage at each of the sites (e.g., CpG sites), thereby determining an aggregate coverage-weighted normalized methylation difference score. In particular embodiments, an aggregate coverage-weighted normalized methylation difference score (represented as A) is determined according to the formula

$A = \frac{\sum_{i = 1}^{k}\; {w_{i}Z_{i}}}{\sqrt{\sum_{i = 1}^{k}\; w_{i}^{2}}}$

wherein w_(i) represents the coverage at site i, and k represents the total number of sites. Use of the formula for determining an aggregate coverage-weighted normalized methylation difference score is exemplified in Example I, below.

In particular embodiments, the methods set forth herein can be used to identify a change in methylation state for a test organism or to monitor such changes over time. Accordingly, the present disclosure provides a method that includes steps of (a) providing a test data set that includes (i) methylation states for a plurality of sites from test genomic DNA from at least one test organism, and (ii) coverage at each of the sites for detection of the methylation states; (b) providing methylation states for the plurality of sites in reference genomic DNA from one or more reference individual organisms, (c) determining, for each of the sites, the methylation difference between the test genomic DNA and the reference genomic DNA, thereby providing a normalized methylation difference for each site; (d) weighting the normalized methylation difference for each site by the coverage at each of the sites, thereby determining an aggregate coverage-weighted normalized methylation difference score and (e) repeating steps (a) through (d) using a second test data set that includes (i) methylation states for the plurality of sites from a second test genomic DNA from the individual test organism, and (ii) coverage at each of the sites for detection of the methylation states, and using the same reference genomic DNA from the at least one reference individual, and (f) determining whether or not a change has occurred in the aggregate coverage-weighted normalized methylation difference score between the test genomic DNA and the second test genomic DNA.

Also provided is a method that includes the steps of (a) providing a sample containing a mixture of genomic DNA from a plurality of different cell types from at least one test organism, thereby providing test genomic DNA; (b) detecting methylation states for a plurality of sites in the test genomic DNA; (c) determining the coverage at each of the sites for the detecting of the methylation states; (d) providing methylation states for the plurality of sites in reference genomic DNA from at least one reference individual, the at least one test organism and reference individual optionally being the same species; (e) determining, for each of the sites, the methylation difference between the test genomic DNA and the reference genomic DNA, thereby providing a normalized methylation difference for each site; (f) weighting the normalized methylation difference for each site by the coverage at each of the sites, thereby determining an aggregate coverage-weighted normalized methylation difference score; (g) repeating steps (a) through (f) using a second test genomic DNA provided from a sample comprising a mixture of genomic DNA from a plurality of different cell types from the at least one test organism, and using the same reference genomic DNA from the at least one reference individual, and (h) determining whether or not a change has occurred in the aggregate coverage-weighted normalized methylation difference score between the test genomic DNA and the second test genomic DNA.

In another embodiment, the method is refined to take into consideration the observed variations in aggregate DNA methylation within a normal population. The test genomic DNA is not compared directly to a reference genomic DNA; rather, an intermediate step is interposed that includes the evaluation of a training set of normal genomic DNA samples against the reference genomic DNA—referred to in this embodiment as baseline samples—to assess variation of aggregate DNA methylation within a normal population. This involves calculating “methylation scores” for each member of a training set of normal genomic DNA samples, and determining the mean and standard deviation of the methylation scores of the training set population, thereby yielding information about the distribution of methylation scores in a normal population. In some embodiments, the number of normal individual organisms providing genomic DNA for the training set is at least 3, at least 5, at least 10, at least 20, at least 50, or at least 100.

In this embodiment, the method can include a first step of determining, for each CpG site i, the mean methylation level (μ_(i)) and standard deviation of methylation levels (σ_(i)), observed for a population of reference genomic DNA. Here, the reference or baseline genomic DNA takes the form of a population of normal genomic DNA samples. A selected genomic DNA can then be compared to the baseline DNA population to evaluate variation in methylation levels. More specifically, methylation levels at each site i (e.g., CpG site) in a selected genomic DNA can be compared to the population mean, μ_(i), for the baseline samples to generate a methylation score for the selected genomic DNA. In one embodiment, the selected genomic DNA is a set of training controls, and in another embodiment, the selected genomic DNA is a test genomic DNA. Methylation levels can be determined by methods that are routine and known to the skilled person. For example, methylation levels can be calculated as the fraction of ‘C’ bases at a target CpG site out of ‘C’+‘U’ bases following the bisulfite treatment, or the fraction of ‘C’ bases at a target CpG site out of total ‘C’+‘T’ bases following the bisulfite treatment and subsequent nucleic acid amplification, as described herein.

A methylation score (MS) for a selected genomic DNA can be calculated by determining the normalized methylation difference (z-score) at a particular site i (e.g., CpG site) with reference to a set of baseline samples, converting the z-score for each site into a probability of observing such a z-score or greater (e.g., a one-sided p-value), and combining the p-values into a final, aggregate methylation score. Optionally, the p-values are weighted. Each of these steps is detailed herein and immediately below.

Methylation scores are initially determined for a training set of normal genomic DNA samples. First, a normalized methylation difference (z-score) at a particular site i (e.g., CpG site) is determined according to the formula

$Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$

wherein Z_(i) represents a normalized methylation difference for a particular site identified as i, χ_(i) represents the methylation level at site i in a member of the training set of normal genomic DNA, μ_(i) represents the mean methylation level at site i in the baseline samples, and σ_(i) represents the standard deviation of methylation levels at site i in the baseline samples.

The z-score for each CpG site i (Z_(i)) is then converted into the probability of observing such a z-score or greater. In one aspect, the probability is calculated by converting the z-score into a one-sided p-value (p_(i)). Probabilities can be calculated assuming a normal distribution, t-distribution, or binomial distribution. Statistical tools for such calculations are well known and easily available to a person of ordinary skill.

Next, a methylation score (MS), an aggregate of the probability of the observed normalized methylation differences, is determined by combining the p-values according to the Fisher formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}\; {\ln \left( p_{i} \right)}}}$

wherein p_(i) represents the one-sided p-value at site i, and k represents the total number of sites. A methylation score is calculated for each member of the training set of normal genomic DNA.

Optionally, the p-value at each CpG site can be weighted by multiplying the p-value at each CpG site i (p_(i)) with a weighting factor w_(i), where w_(i) can correspond to the significance of the CpG site obtained from a priori knowledge, the depth of coverage associated with the site, or any other ranking method. In this aspect, a methylation score (represented as MS) is determined by combining the weighted p-values according to the Fisher formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}\; {\ln \left( {w_{i}p_{i}} \right)}}}$

wherein p_(i) represents the one-sided p-value at site i, k represents the total number of sites, and w_(i) represents the significance, for instance coverage, of the site i. Use of this formula for determining weighted methylation scores for a training set of normal genomic DNA samples is illustrated in Example III.

Statistical analysis of the training set methylation scores is then performed. The mean methylation score (μ_(MS)) and standard deviation of methylation scores (σ_(MS)) in the training set of normal genomic DNA are calculated. This characterizes the distribution of the methylation score in a normal population, and can be used to determine whether the genomic DNA of a test genomic sample has an aberrant methylation level.

The methylation score (MS) of a test genomic DNA is then determined with reference to the baseline samples (as described above for members of the training set) and compared to the distribution of the methylation scores determined for the training set of normal genomic DNA.

As described above in connection with the training set, a normalized methylation difference (z-score) at a particular site i (e.g., CpG site) is first determined according to the formula

$Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$

wherein Z_(i) represents a normalized methylation difference for a particular site identified as i, χ_(i) represents the methylation level at site i in the test genomic DNA, μ_(i) represents the mean methylation level at site i in the baseline samples, and σ_(i) represents the standard deviation of methylation levels at site i in the baseline samples.

The z-score for each CpG site i (Z_(i)) is then converted into the probability of observing such a z-score or greater. In one aspect, the probability is calculated by converting the z-score into a one-sided p-value (p_(i)). Probabilities can be calculated assuming a normal distribution, t-distribution, or binomial distribution. A methylation score (MS) of the test genomic DNA is determined by combining the p-values according to the Fisher formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}\; {\ln \left( p_{i} \right)}}}$

wherein p_(i) represents the one-sided p-value at site i, and k represents the total number of sites.

Optionally, the p-value at each CpG site can be weighted by multiplying the p-value at each CpG site i (p_(i)) with a weight w_(i), where w_(i) can correspond to the significance of the CpG site obtained from a priori knowledge, the depth of coverage associated with the site, or any other ranking method. A methylation score (MS) of the test genomic DNA is determined by combining the weighted p-values according to the Fisher formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}\; {\ln \left( {w_{i}p_{i}} \right)}}}$

wherein p_(i) represents the one-sided p-value at site i, k represents the total number of sites, and w_(i) represents the significance, for instance coverage, of the site i. Use of this formula for determining weighted methylation scores for test genomic DNA samples is illustrated in Examples II and III.

Finally, the methylation score of the test genomic DNA is evaluated against the distribution of methylation scores determined for the training set population, represented by the mean methylation score (μ_(MS)) and standard deviation of methylation scores (σ_(MS)) for the training set of normal genomic DNA. The number of standard deviations the methylation score for the test genomic DNA is from the methylation score mean (μ_(MS)) of the training set of normal genomic DNA is determined according to the formula

$Z_{MS} = \frac{{MS} - µ_{MS}}{\sigma_{MS}}$

wherein Z_(MS) represents a normalized methylation score difference, MS represents the methylation score of the test sample, μ_(MS) represents the mean methylation score for the training set of normal genomic DNA, and σ_(MS) represents the standard deviation of methylation scores for the training set of normal genomic DNA. Use of this formula for determining normalized methylation score difference is illustrated in Example III. A Z_(MS) value of greater than 1.5, greater than 2, greater than 2.5, or greater than 3 standard deviations indicates the test genomic DNA has an aberrant DNA methylation level. In a preferred embodiment, a Z_(MS) value greater than 3 standard deviations is used as an indication that the test genomic DNA has an aberrant DNA methylation level.

In another embodiment, the methods set forth herein can be used to identify a change in methylation state for a test organism or to monitor such changes over time. Accordingly, the present disclosure provides a method that includes steps of (a) providing methylation states for a plurality of sites (e.g., CpG sites) in baseline genomic DNA from two or more normal individual organisms; (b) determining, for each of the sites (e.g., CpG sites), the mean methylation level and standard deviation of methylation levels for the baseline genomic DNA; (c) providing a test data set that includes (i) methylation states for the plurality of sites (e.g., CpG sites) from a first test genomic DNA from at least one test organism, and optionally (ii) coverage at each of the sites (e.g., CpG sites) for detection of the methylation states; (d) determining, for each of the sites (e.g., CpG sites), the methylation difference between the first test genomic DNA and the baseline genomic DNA, thereby providing a normalized methylation difference for the first test genomic DNA; (e) converting the normalized methylation difference for the first test genomic DNA at each of the sites (e.g., CpG sites) into the probability of observing such a normalized methylation difference or greater (e.g., a one-sided p-value), and optionally weighting the probability of such an event; (f) determining a methylation score for the first test genomic DNA; (g) repeating steps (c) through (f) using a second test genomic DNA provided from a sample comprising a mixture of genomic DNA from a plurality of different cell types from the at least one test organism, and using the same baseline genomic DNA; and (h) determining whether or not a change has occurred in the methylation score between the first test genomic DNA and the second test genomic DNA.

An alternative method of monitoring changes in DNA methylation over time includes the steps of (a) providing methylation states for a plurality of sites (e.g., CpG sites) in baseline genomic DNA from two or more normal individual organisms; (b) determining, for each of the sites (e.g., CpG sites), the mean methylation level and standard deviation of methylation levels for the baseline genomic DNA; (c) providing a mixture of genomic DNA from a test organism suspected of having a condition associated with an aberrant DNA methylation level (e.g., cancer), wherein the mixture comprises genomic DNA from a plurality of different cell types from the test organism, thereby providing a first test genomic DNA; (d) detecting methylation states for the plurality of sites (e.g., CpG sites) in the first test genomic DNA, and optionally determining the coverage at each of the sites (e.g., CpG sites) for the detecting of the methylation states; (e) determining, for each of the sites (e.g., CpG sites), the methylation difference between the first test genomic DNA and the baseline genomic DNA, thereby providing a normalized methylation difference for the first test genomic DNA; (f) converting the normalized methylation difference for the first test genomic DNA at each of the sites (e.g., CpG sites) into the probability of observing such a normalized methylation difference or greater (e.g., a one-sided p-value), and optionally weighting the probability of such an event; (g) determining a methylation score for the first test genomic DNA; (h) repeating steps (c) through (g) using a second test genomic DNA provided from a sample comprising a mixture of genomic DNA from a plurality of different cell types from the at least one test organism, and using the same baseline genomic DNA; and (i) determining whether or not a change has occurred in the methylation score between the first test genomic DNA and the second test genomic DNA.

First and second test genomic DNA samples (or test data sets) that are compared in a method set forth herein can be derived from the same type of cell, community, tissue or fluid, but at different time points. Accordingly, a method set forth herein can be used to identify or monitor a change that occurs over time. In some embodiments the different time points can occur before, during and/or after a particular treatment. For example, in the case of monitoring or prognosing cancer, samples can be obtained from an individual before and after initiation of a treatment such as surgery, chemotherapy or radiation therapy. Furthermore multiple samples can be obtained at different time points during treatment. For example the samples can be obtained and evaluated at time points throughout surgery (e.g. to evaluate whether or not margins have been cleared of cancerous tissue) or at different time points throughout a course of chemotherapy or radiation therapy. Different samples can be obtained from an individual and tested after treatment for example to test for relapse and remission.

In a further example, gut metagenomics samples can be obtained before and after a treatment (e.g. for a digestive disorder). The methylation states of the samples can be evaluated and compared to identify changes in the bacterial flora of the gut due to the treatment. The changes in turn can be used to monitor the treatment and determine a prognosis for the individual being treated.

Any of a variety of sample types set forth herein, or known in the art to contain tumor DNA, can be used in a method for identifying or monitoring a change in methylation state for an individual. Observed changes can provide a basis for diagnosis, prognosis, or screening of an individual with respect to having a particular condition such as cancer.

A method set forth herein can also be used to screen or test a candidate treatment, for example, in an experimental cell culture, tissue or organism. Accordingly, a method set forth herein can be used to identify or monitor a change that occurs over time in a cell culture, tissue or organism being tested in a clinical or laboratory environment. In some embodiments the different time points can occur before, during and/or after a particular candidate treatment. For example, samples can be obtained from a test organism before and after initiation of a candidate treatment such as surgery, chemotherapy or radiation therapy. Furthermore, multiple samples can be obtained at different time points during the candidate treatment. For example the samples can be obtained and evaluated at time points throughout surgery (e.g. to evaluate whether or not margins have been cleared of cancerous tissue) or at different time points throughout a course of a candidate chemotherapy or radiation therapy. Different samples can be obtained from a test organism and tested after a candidate treatment, for example, to evaluate relapse and remission. Control organisms that are not subjected to the candidate treatment and/or that do not have a particular condition can also be tested using similar methods. Comparison of results between samples subjected to candidate treatments and controls can be used to determine efficacy and/or safety of a particular candidate treatment

Any of a variety of sample types set forth herein, or known in the art to contain tumor DNA, can be used in a method for identifying or screening a candidate treatment. Changes, whether or not being compared to a particular control, can be used for evaluating efficacy and/or safety of a particular candidate treatment.

In particular embodiments, this disclosure provides a method for detecting a condition such as cancer. The method can include steps of (a) providing a mixture of genomic DNA from an individual suspected of having the condition (e.g. cancer), wherein the mixture comprises genomic DNA from a plurality of different cell types from the individual, thereby providing test genomic DNA; (b) detecting methylation states for a plurality of sites (e.g. CpG sites) in the test genomic DNA; (c) determining the coverage at each of the sites (e.g. CpG sites) for the detecting of the methylation states; (d) providing methylation states for the plurality of sites (e.g. CpG sites) in reference genomic DNA from at least one reference individual, the reference individual being known to have the condition (e.g. cancer) or known to not have the condition (e.g. cancer); (e) determining, for each of the sites (e.g. CpG sites), the methylation difference between the test genomic DNA and the reference genomic DNA, thereby providing a normalized methylation difference for each site (e.g. CpG site); (f) weighting the normalized methylation difference for each site (e.g. CpG site) by the coverage at each of the sites (e.g. CpG sites), thereby determining an aggregate coverage-weighted normalized methylation difference score; and (g) determining that the individual does or does not have the condition (e.g. cancer) based on the aggregate coverage-weighted normalized methylation difference score. In some embodiments the sample is blood and the DNA can, for example, include cell free DNA from the blood.

Also provided is a method for identifying a change in a condition such as cancer. The method can include steps of (a) providing a mixture of genomic DNA from an individual suspected of having the condition (e.g. cancer), wherein the mixture comprises genomic DNA from a plurality of different cell types from the individual, thereby providing test genomic DNA; (b) detecting methylation states for a plurality of sites (e.g. CpG sites) in the test genomic DNA; (c) determining the coverage at each of the sites (e.g. CpG sites) for the detecting of the methylation states; (d) providing methylation states for the plurality of sites (e.g. CpG sites) in reference genomic DNA from at least one reference individual, the reference individual being known to have the condition (e.g. cancer) or known to not have the condition (e.g. cancer); (e) determining, for each of the sites (e.g. CpG sites), the methylation difference between the test genomic DNA and the reference genomic DNA, thereby providing a normalized methylation difference for each site (e.g. CpG site); (f) weighting the normalized methylation difference for each site (e.g. CpG site) by the coverage at each of the sites (e.g. CpG sites), thereby determining an aggregate coverage-weighted normalized methylation difference score; and (g) repeating steps (a) through (f) using a second mixture of genomic DNA from the individual suspected of having the condition (e.g. cancer), and using the same reference genomic DNA from the at least one reference individual, and (h) determining whether or not a change has occurred in the aggregate coverage-weighted normalized methylation difference score for the second test genomic DNA compared to the test genomic DNA, thereby determining that a change has or has not occurred in the condition (e.g. cancer) based on the change in the aggregate coverage-weighted normalized methylation difference score.

In particular embodiments, this disclosure provides a method for detecting a condition such as cancer. The method can include steps of (a) providing methylation states for a plurality of sites (e.g., CpG sites) in baseline genomic DNA from at least one normal individual organism; (b) determining, for each of the sites (e.g., CpG sites), the mean methylation level and standard deviation of methylation levels for the baseline genomic DNA; (c) providing a training set of normal genomic DNA samples from two or more normal individual organisms that includes (i) methylation states for a plurality of sites (e.g., CpG sites) in the training set of normal genomic DNA samples, and optionally (ii) coverage at each of the sites (e.g., CpG sites) for detection of the methylation states; (d) determining, for each of the sites (e.g., CpG sites), the methylation difference between each normal genomic DNA sample of the training set and the baseline genomic DNA, thereby providing a normalized methylation difference for each normal genomic DNA sample of the training set at each site (e.g., CpG site); (e) converting the normalized methylation difference for each normal genomic DNA sample of the training set at each site (e.g., CpG site) into the probability of observing such a normalized methylation difference or greater (e.g., a one-sided p-value), and optionally weighting the probability of such an event; (f) determining a methylation score for each normal genomic DNA sample of the training set to obtain training set methylation scores; (g) calculating the mean methylation score and standard deviation of the training set methylation scores; (h) providing a mixture of genomic DNA from a test organism suspected of having the condition (e.g., cancer), wherein the mixture comprises genomic DNA from a plurality of different cell types from the test organism, thereby providing test genomic DNA; (i) detecting methylation states for the plurality of sites (e.g., CpG sites) in the test genomic DNA, and optionally determining the coverage at each of the sites (e.g., CpG sites) for the detecting of the methylation states; (j) determining, for each of the sites (e.g., CpG sites), the methylation difference between the test genomic DNA and the baseline genomic DNA, thereby providing a normalized methylation difference for the test genomic DNA; (k) converting the normalized methylation difference for the test genomic DNA at each of the sites (e.g., CpG sites) into the probability of observing such a normalized methylation difference or greater (e.g., a one-sided p-value), and optionally weighting the probability of such an event; (1) determining a methylation score for the test genomic DNA; and (m) comparing the methylation score of the test genomic DNA to the mean methylation score and standard deviation of methylation scores in the training set of normal genomic DNA to determine the number of standard deviations the methylation score of the test genomic DNA is from the distribution of methylation scores in the training set of normal genomic DNA. In the event the number of standard deviations exceeds a predetermined threshold value (e.g., 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, etc.), the test sample is considered to have an aberrant DNA methylation level.

Optionally, the sites from the test genomic DNA are derived from a plurality of different cell types from the individual test organism, and as a further option, the cell type from which each of the sites (e.g., CpG sites) is derived is unknown. In a further optional embodiment, the individual test organism and the one or more baseline individual organisms, training individual organisms, or a combination thereof are the same species. In some embodiments, the mixture of genomic DNA from an individual suspected of having the condition is blood and the DNA can, for example, include cell-free DNA (cfDNA) or circulating tumor DNA (ctDNA) from the blood.

Also provided herein is a method for identifying a change in a condition such as cancer over time. The method can include steps of (a) providing methylation states for a plurality of sites (e.g., CpG sites) in baseline genomic DNA from at least one normal individual organism; (b) determining, for each of the sites (e.g., CpG sites), the mean methylation level and standard deviation of methylation levels for the baseline genomic DNA; (c) providing a first mixture of genomic DNA from a test organism suspected of having the condition (e.g., cancer), wherein the first mixture comprises genomic DNA from a plurality of different cell types from the test organism, thereby providing a first test genomic DNA; (d) detecting methylation states for the plurality of sites (e.g., CpG sites) in the first test genomic DNA, and optionally determining the coverage at each of the sites (e.g., CpG sites) for the detecting of the methylation states; (e) determining, for each of the sites (e.g., CpG sites), the methylation difference between the first test genomic DNA and the baseline genomic DNA, thereby providing a normalized methylation difference for the first test genomic DNA; (f) converting the normalized methylation difference for the first test genomic DNA at each of the sites (e.g., CpG sites) into the probability of observing such a normalized methylation difference or greater (e.g., a one-sided p-value), and optionally weighting the probability of such an event; (g) determining a methylation score for the first test genomic DNA; (h) repeating steps (c) through (g) using a second mixture of genomic DNA from the test organism suspected of having the condition (e.g., cancer), wherein the second mixture comprises a second test genomic DNA, and (i) determining whether or not a change has occurred in the methylation score for the second test genomic DNA compared to the first test genomic DNA, thereby determining that a change has or has not occurred in the condition (e.g., cancer) based on the change in the methylation score.

Methylation states determined using methods set forth herein can be used for molecular classification and prediction of cancers using criteria that have been developed for gene expression and other genomic data (see, for example, Golub et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286, 531-537.). Other classification systems that can be used include those that have been developed for correlating global changes in methylation pattern to molecular classification in breast cancer (see, for example, Huang et al. (1999) Methylation profiling of CpG sites in human breast cancer cells. Hum Mol Genet, 8, 459-470), or those developed for correlating methylation patterns in tumor suppressor genes (for example, p16, a cyclin-dependent kinase inhibitor) in certain human cancer types (see, for example, Herman et al. (1995) Inactivation of the CDKN2/p16/MTS1 gene is frequently associated with aberrant DNA methylation in all common human cancers. Cancer Res, 55, 4525-4530.; Otterson et al. (1995) CDKN2 gene silencing in lung cancer by DNA hypermethylation and kinetics of p16INK4 protein induction by 5-aza 2′deoxycytidine. Oncogene, 11, 1211-1216.). The above references are incorporated herein by reference.

In some applications of the methylation analysis methods set forth herein, a model can be developed to predict the disease type without prior pathological diagnosis. Thus, in some embodiments, the methods set forth herein are used to determine methylation patterns in staged tumor samples relative to matched normal tissues from the same patient. The determined differences in methylation pattern between the tumor and normal tissues can be used to build a model to predict, diagnose or monitor cancer. For example, methylation patterns determined for a test sample can be compared to a methylation pattern from a known normal and/or from a known tumor, and a diagnosis can be made based on the degree of similarity of the test sample to one or both of these references.

In addition, the methods set forth herein can facilitate identification, classification and prognostic evaluation of tumors. This information can in turn be used to identify subgroups of tumors with related properties. Such classification has been useful in identifying the causes of various types of cancer and in predicting their clinical behavior.

In particular embodiments of the present methods, cancers are predicted, detected, identified, classified, or monitored from cell free DNA of cancer patients. For example, the determination of a methylation pattern from a plasma sample can be used to screen for cancer. When the methylation pattern of the plasma sample is aberrant compared with a healthy reference, cancer may be suspected. Then further confirmation and assessment of the type of cancer or tissue origin of the cancer can be performed by determining the plasma profile of methylation at different genomic loci or by plasma genomic analysis to detect tumor-associated copy number aberrations, chromosomal translocations and single nucleotide variants. Alternatively, radiological and imaging investigations (e.g. computed tomography, magnetic resonance imaging, positron emission tomography) or endoscopy (e.g. upper gastrointestinal endoscopy or colonoscopy) can be used to further investigate individuals who were suspected of having cancer based on the plasma methylation level analysis.

In one aspect of the present invention, provided herein is a method for using methylation levels to identify or classify a specific type of cancer in a test organism, preferably a mammalian organism, more preferably a human. In this aspect, methylation levels of a test genomic DNA are evaluated, for subsets of preselected methylation sites associated with known cancer types, herein referred to as “hypermethylated” sites, and then ranked from lowest to highest. The cancer type corresponding to the highest average methylation level is considered to be associated with the test genomic DNA, i.e. the cancer type is deemed to be present in the test organism.

As a starting point, the method can include identifying specific cancers that can be used as a cancer type in the identification or classification algorithm according to this aspect of the invention. A cancer type is a cancer, e.g., breast invasive carcinoma, colon adenocarcinoma, lung adenocarcinoma, and others, that can be used as a member of a panel of specific cancers to determine whether a test organism has a specific type of cancer.

Determining whether a cancer can be used as a cancer type in the present method includes obtaining genomic DNA sequence data from clinical samples. Genomic DNA sequence data useful herein is readily available from known databases that characterize genomic and epigenomic changes—such as changes in methylation state—in different types of cancers. The greater the number of clinical samples of a cancer in a database, the more likely the cancer can be used as a cancer type. A cancer type suitable for the present method may be defined using genomic DNA sequence data from at least 10, at least 15, at least 20, at least 25, at least 30, at least 40, at least 50, at least 75, or at least 100 clinical samples of a specific cancer.

Once a panel of suitable cancer type has been defined, a list of so-called “hypermethylated” sites specific for each cancer type is assembled. In some embodiments, useful methylation sites that can be evaluated for methylation state include the selected CpG sites of the Pan Cancer Panel set forth in Table I (the listed methylation sites are from Genome Build 37) and/or set forth in Table II (the listed methylation sites are from Genome Build 37). In other embodiments, useful methylation sites that can be evaluated for methylation state include those present in The Cancer Genome Atlas (see, for example, Cancer Genome Atlas Research Network et al., Nature Genetics 45:1113-1120 (2013)), the CpG sites used to identify or monitor colorectal cancer described in Worthley et al., Oncogene 29, 1653-1662 (2010), and methylation markers for detection of ovarian cancer set forth in US Pat. App. Pub. No. 2008/0166728 A1, among others. All of the cited documents are incorporated herein by reference in their entireties. All or a subset of the sites set forth herein or listed in a reference herein can be used in the identification or classification method set forth herein. For example, at least 100, 1×10³, 1×10⁴, 1×10⁵, 1×10⁶, or more of the methylation sites can be used as a starting point. In some embodiments, the entire methylome (i.e. the full set of methylation sites in a test organism's genome) may be used to select hypermethylated sites suitable for the present method.

TABLE I Pan Cancer Panel cg00006948 cg00012992 cg00019137 cg00021108 cg00026375 cg00027037 cg00039627 cg00041084 cg00056676 cg00059034 cg00073771 cg00073780 cg00079563 cg00081574 cg00091964 cg00107016 cg00114393 cg00114963 cg00115040 cg00117463 cg00121634 cg00121640 cg00124160 cg00128353 cg00132108 cg00134776 cg00136947 cg00139244 cg00141174 cg00143220 cg00145489 cg00151810 cg00155423 cg00157987 cg00158254 cg00164196 cg00168514 cg00169305 cg00183340 cg00196372 cg00202702 cg00205263 cg00207389 cg00208931 cg00210994 cg00214530 cg00220517 cg00221969 cg00233079 cg00235260 cg00235337 cg00245538 cg00246817 cg00251610 cg00254802 cg00259618 cg00262031 cg00264591 cg00266918 cg00267325 cg00275232 cg00280758 cg00281977 cg00283576 cg00286984 cg00288050 cg00289081 cg00291351 cg00302521 cg00303548 cg00303672 cg00310855 cg00311654 cg00312474 cg00318608 cg00322319 cg00325599 cg00338893 cg00341980 cg00344260 cg00346326 cg00350003 cg00351011 cg00352349 cg00353340 cg00358220 cg00365470 cg00367047 cg00370303 cg00371920 cg00372486 cg00381697 cg00389976 cg00395632 cg00397851 cg00401880 cg00404838 cg00405843 cg00407729 cg00408906 cg00414171 cg00414398 cg00415978 cg00419564 cg00440043 cg00442814 cg00447632 cg00449821 cg00450312 cg00456894 cg00466108 cg00466364 cg00470794 cg00471966 cg00474209 cg00476317 cg00480136 cg00483446 cg00485296 cg00485849 cg00486611 cg00486627 cg00487870 cg00488787 cg00489861 cg00495503 cg00498155 cg00499289 cg00503704 cg00504703 cg00507727 cg00512374 cg00525503 cg00527440 cg00533620 cg00549463 cg00549910 cg00551736 cg00552973 cg00559018 cg00560547 cg00562243 cg00567696 cg00574530 cg00576301 cg00577109 cg00581731 cg00582881 cg00583303 cg00586537 cg00588720 cg00591844 cg00593962 cg00594560 cg00594866 cg00598730 cg00603617 cg00607526 cg00611485 cg00613753 cg00614641 cg00616965 cg00618725 cg00622677 cg00626110 cg00633736 cg00639886 cg00642494 cg00643111 cg00650006 cg00651829 cg00656387 cg00656411 cg00658626 cg00659495 cg00661753 cg00663739 cg00673557 cg00679738 cg00683895 cg00687122 cg00691999 cg00692763 cg00695712 cg00697033 cg00702008 cg00704633 cg00708380 cg00709515 cg00712044 cg00713925 cg00719143 cg00720475 cg00735962 cg00741789 cg00744920 cg00745606 cg00747619 cg00751156 cg00752016 cg00752376 cg00755836 cg00757182 cg00758584 cg00761787 cg00769520 cg00769843 cg00773459 cg00780574 cg00790649 cg00793280 cg00796360 cg00796793 cg00797346 cg00800993 cg00803088 cg00803816 cg00808740 cg00809888 cg00813603 cg00815093 cg00818693 cg00819163 cg00821073 cg00824141 cg00828602 cg00834400 cg00835429 cg00838874 cg00843352 cg00845942 cg00846483 cg00850971 cg00868383 cg00873850 cg00877887 cg00879003 cg00879447 cg00880074 cg00884221 cg00887511 cg00894435 cg00896540 cg00899483 cg00901765 cg00906644 cg00907211 cg00918541 cg00919118 cg00929855 cg00939301 cg00940278 cg00944142 cg00948275 cg00953355 cg00953777 cg00954566 cg00964103 cg00965391 cg00966974 cg00969047 cg00969787 cg00971804 cg00976157 cg00977805 cg00979348 cg00983637 cg00984694 cg00992385 cg00994693 cg00996262 cg00996986 cg00999950 cg01009697 cg01012280 cg01015652 cg01016553 cg01016662 cg01019028 cg01024009 cg01025398 cg01029840 cg01033463 cg01035198 cg01043524 cg01045612 cg01052477 cg01054622 cg01060026 cg01060059 cg01069256 cg01070355 cg01070794 cg01076997 cg01078147 cg01079098 cg01079658 cg01083689 cg01093319 cg01097611 cg01097881 cg01097964 cg01098237 cg01099231 cg01099875 cg01101742 cg01105385 cg01107801 cg01108392 cg01112082 cg01112965 cg01126855 cg01141237 cg01142386 cg01143579 cg01143804 cg01145317 cg01151699 cg01152936 cg01153132 cg01156948 cg01157070 cg01160085 cg01175682 cg01179851 cg01181350 cg01183053 cg01184522 cg01186777 cg01187533 cg01191114 cg01196517 cg01204271 cg01211349 cg01215936 cg01216370 cg01224730 cg01226806 cg01227006 cg01228636 cg01236148 cg01244034 cg01244650 cg01245656 cg01246835 cg01247426 cg01250961 cg01254575 cg01256674 cg01258587 cg01259619 cg01263075 cg01263292 cg01263716 cg01267522 cg01268683 cg01269344 cg01269537 cg01270246 cg01274625 cg01275523 cg01277490 cg01280202 cg01284881 cg01289643 cg01294263 cg01300341 cg01307939 cg01308268 cg01310019 cg01310600 cg01313313 cg01313518 cg01315063 cg01316109 cg01327552 cg01333350 cg01335685 cg01335781 cg01339444 cg01340952 cg01341170 cg01363902 cg01367393 cg01369082 cg01370014 cg01370063 cg01370181 cg01372071 cg01373292 cg01377006 cg01380710 cg01384163 cg01385795 cg01387945 cg01394093 cg01398050 cg01402994 cg01404988 cg01406536 cg01409659 cg01414358 cg01418667 cg01419567 cg01421405 cg01424281 cg01429635 cg01431908 cg01434487 cg01442844 cg01445942 cg01446203 cg01450204 cg01451328 cg01453694 cg01458686 cg01460805 cg01462053 cg01464969 cg01466678 cg01479818 cg01483681 cg01485010 cg01486752 cg01492246 cg01498231 cg01499217 cg01504555 cg01505590 cg01505767 cg01506130 cg01506492 cg01507044 cg01507046 cg01511379 cg01514859 cg01518631 cg01522456 cg01532206 cg01544751 cg01548300 cg01549404 cg01555604 cg01556502 cg01558040 cg01562471 cg01563031 cg01564068 cg01565690 cg01566304 cg01573616 cg01583034 cg01583969 cg01587630 cg01588748 cg01593751 cg01597066 cg01601746 cg01606085 cg01606998 cg01607295 cg01611886 cg01612478 cg01613306 cg01616178 cg01616474 cg01618114 cg01618829 cg01632300 cg01637011 cg01637551 cg01641096 cg01645113 cg01646610 cg01646639 cg01649597 cg01653005 cg01656221 cg01657408 cg01665212 cg01666600 cg01667646 cg01667837 cg01677561 cg01678172 cg01678714 cg01682021 cg01692340 cg01699584 cg01706029 cg01706789 cg01719157 cg01719793 cg01722423 cg01722994 cg01725608 cg01740172 cg01740424 cg01742897 cg01743617 cg01743962 cg01754037 cg01754155 cg01757209 cg01763173 cg01764954 cg01772385 cg01775260 cg01778450 cg01780109 cg01782227 cg01785417 cg01791407 cg01794473 cg01814098 cg01814945 cg01822124 cg01831896 cg01833212 cg01834210 cg01839688 cg01844352 cg01844539 cg01851208 cg01857475 cg01861574 cg01862172 cg01869273 cg01869826 cg01871428 cg01873234 cg01876194 cg01885963 cg01890984 cg01893041 cg01908537 cg01911237 cg01912921 cg01922936 cg01940943 cg01947949 cg01948390 cg01949798 cg01950665 cg01952683 cg01955962 cg01962509 cg01962510 cg01966612 cg01967288 cg01969058 cg01970519 cg01970575 cg01970784 cg01978544 cg01981187 cg01984858 cg01992107 cg01995821 cg01998047 cg02002584 cg02005505 cg02012576 cg02012731 cg02014107 cg02020882 cg02028389 cg02030493 cg02033141 cg02034497 cg02048890 cg02052895 cg02059348 cg02062409 cg02065704 cg02072885 cg02078870 cg02079933 cg02086858 cg02096492 cg02101625 cg02110141 cg02114924 cg02115050 cg02115539 cg02120463 cg02120582 cg02126753 cg02128087 cg02132163 cg02132470 cg02134353 cg02136132 cg02138756 cg02144298 cg02151754 cg02162906 cg02169113 cg02169734 cg02172579 cg02174225 cg02180498 cg02196227 cg02200939 cg02202600 cg02202980 cg02205739 cg02215070 cg02219071 cg02221750 cg02221866 cg02229097 cg02229993 cg02231729 cg02232273 cg02233216 cg02236651 cg02241397 cg02247561 cg02248320 cg02250071 cg02251557 cg02257090 cg02257750 cg02260353 cg02265056 cg02266348 cg02270183 cg02273903 cg02277383 cg02282626 cg02284150 cg02284587 cg02285922 cg02286547 cg02290238 cg02293228 cg02298956 cg02303897 cg02304863 cg02306127 cg02307033 cg02307605 cg02310733 cg02315971 cg02316216 cg02328010 cg02330121 cg02330494 cg02331143 cg02340915 cg02344926 cg02346970 cg02352687 cg02352723 cg02353937 cg02357043 cg02362467 cg02362970 cg02369195 cg02384967 cg02388709 cg02394263 cg02398045 cg02398612 cg02399645 cg02400449 cg02405503 cg02406285 cg02407785 cg02408333 cg02409108 cg02424378 cg02425263 cg02430347 cg02435495 cg02445664 cg02447380 cg02448922 cg02454595 cg02460997 cg02461665 cg02470625 cg02472291 cg02474799 cg02481778 cg02483484 cg02485200 cg02486351 cg02487654 cg02492778 cg02492791 cg02503395 cg02506053 cg02510164 cg02511156 cg02513017 cg02513409 cg02527199 cg02532538 cg02537163 cg02539714 cg02547025 cg02551396 cg02552311 cg02554246 cg02557406 cg02557432 cg02558627 cg02560717 cg02565702 cg02567082 cg02574526 cg02583334 cg02584489 cg02593205 cg02595750 cg02596331 cg02602699 cg02604121 cg02608019 cg02617469 cg02617655 cg02618553 cg02620228 cg02620694 cg02622885 cg02624855 cg02627531 cg02628801 cg02630553 cg02633073 cg02636497 cg02637978 cg02638057 cg02639285 cg02639993 cg02643218 cg02649987 cg02651961 cg02654360 cg02655739 cg02657292 cg02659086 cg02659794 cg02660823 cg02664993 cg02665570 cg02666434 cg02666504 cg02669964 cg02671646 cg02673256 cg02687055 cg02688760 cg02690609 cg02692405 cg02701278 cg02708401 cg02711801 cg02712555 cg02713068 cg02713266 cg02716516 cg02717437 cg02723311 cg02725055 cg02737782 cg02738081 cg02739708 cg02750883 cg02757194 cg02759151 cg02761345 cg02767177 cg02767539 cg02767960 cg02770946 cg02776035 cg02776314 cg02783889 cg02784301 cg02787320 cg02795700 cg02796773 cg02797548 cg02816363 cg02819231 cg02820717 cg02831090 cg02835214 cg02836020 cg02836541 cg02838118 cg02841941 cg02842629 cg02850812 cg02854695 cg02855409 cg02862354 cg02862904 cg02866454 cg02867728 cg02871940 cg02873868 cg02874371 cg02876237 cg02877791 cg02882044 cg02883595 cg02885694 cg02886549 cg02888838 cg02888906 cg02891774 cg02892350 cg02892595 cg02892898 cg02893482 cg02899206 cg02905065 cg02906238 cg02915746 cg02916964 cg02917917 cg02918146 cg02918224 cg02921003 cg02921269 cg02921583 cg02927655 cg02929073 cg02930242 cg02933119 cg02934500 cg02938682 cg02939019 cg02942594 cg02945007 cg02951059 cg02951206 cg02951568 cg02952978 cg02954735 cg02955219 cg02958718 cg02962318 cg02968116 cg02971481 cg02977761 cg02978421 cg02980693 cg02982690 cg02982793 cg02983203 cg02993259 cg02995055 cg03001116 cg03001832 cg03003689 cg03004714 cg03015433 cg03016097 cg03016991 cg03024517 cg03024536 cg03031959 cg03038003 cg03040279 cg03052869 cg03053575 cg03054643 cg03057213 cg03059112 cg03060802 cg03065202 cg03071143 cg03081134 cg03082580 cg03088791 cg03089869 cg03096401 cg03098937 cg03100040 cg03103035 cg03103770 cg03108238 cg03113285 cg03116642 cg03122735 cg03125329 cg03132532 cg03141007 cg03141069 cg03141620 cg03143697 cg03143742 cg03147990 cg03148427 cg03153658 cg03157531 cg03159947 cg03165343 cg03168582 cg03169767 cg03170611 cg03175305 cg03181829 cg03188118 cg03189990 cg03191830 cg03192963 cg03202738 cg03209812 cg03210277 cg03217173 cg03223126 cg03223733 cg03238298 cg03255556 cg03257575 cg03259494 cg03265944 cg03273700 cg03279535 cg03288419 cg03290040 cg03297593 cg03307911 cg03309367 cg03309726 cg03311339 cg03311459 cg03313212 cg03315058 cg03321003 cg03324578 cg03328664 cg03332113 cg03334130 cg03334540 cg03342084 cg03342530 cg03347559 cg03347944 cg03352181 cg03356115 cg03356760 cg03364193 cg03365985 cg03366439 cg03366925 cg03369344 cg03369477 cg03382304 cg03383295 cg03386480 cg03387066 cg03391040 cg03392673 cg03393966 cg03397750 cg03405315 cg03410359 cg03410436 cg03411979 cg03415695 cg03424727 cg03425504 cg03427543 cg03430348 cg03430923 cg03431079 cg03434847 cg03437204 cg03443751 cg03446195 cg03450370 cg03455458 cg03462055 cg03465206 cg03467027 cg03468349 cg03470396 cg03472672 cg03476291 cg03479657 cg03485262 cg03495059 cg03496713 cg03502284 cg03506609 cg03506979 cg03513246 cg03521258 cg03524308 cg03525011 cg03526256 cg03532274 cg03535663 cg03536983 cg03537779 cg03544918 cg03545133 cg03547745 cg03552151 cg03552992 cg03554817 cg03556393 cg03556653 cg03559229 cg03562044 cg03577052 cg03586803 cg03598499 cg03603214 cg03603951 cg03604840 cg03607117 cg03607359 cg03608167 cg03609148 cg03609308 cg03611007 cg03612722 cg03613077 cg03615913 cg03621100 cg03626278 cg03626734 cg03631864 cg03638874 cg03648780 cg03650154 cg03662422 cg03663746 cg03668475 cg03673687 cg03675739 cg03681341 cg03691812 cg03694261 cg03695666 cg03699307 cg03701001 cg03701745 cg03704912 cg03707948 cg03710481 cg03711182 cg03712038 cg03717315 cg03719634 cg03721976 cg03722871 cg03735847 cg03738134 cg03741406 cg03751813 cg03753681 cg03753849 cg03754311 cg03756448 cg03757145 cg03757871 cg03767822 cg03768777 cg03770147 cg03771448 cg03774026 cg03776464 cg03778788 cg03780545 cg03785281 cg03786924 cg03801902 cg03802907 cg03806238 cg03808158 cg03809147 cg03817671 cg03817911 cg03818920 cg03818977 cg03818992 cg03822259 cg03824617 cg03836414 cg03839661 cg03843031 cg03846951 cg03860020 cg03860859 cg03861105 cg03863616 cg03871549 cg03880509 cg03884587 cg03884792 cg03894174 cg03896436 cg03899372 cg03900646 cg03901784 cg03909781 cg03911494 cg03920233 cg03921179 cg03921416 cg03921599 cg03927893 cg03929741 cg03930088 cg03938598 cg03940620 cg03947464 cg03954442 cg03961800 cg03974423 cg03978375 cg03979582 cg03980991 cg03985136 cg03986989 cg03991848 cg03998871 cg04005725 cg04008429 cg04010471 cg04011182 cg04012592 cg04012924 cg04017769 cg04022379 cg04027074 cg04028634 cg04044297 cg04046599 cg04049102 cg04051458 cg04054012 cg04057016 cg04058593 cg04072843 cg04073970 cg04076682 cg04083712 cg04083751 cg04083753 cg04085025 cg04089426 cg04092682 cg04095732 cg04099652 cg04106782 cg04110544 cg04112845 cg04125371 cg04133572 cg04134305 cg04140862 cg04141379 cg04145287 cg04148762 cg04156369 cg04159901 cg04167903 cg04171539 cg04171853 cg04175417 cg04176674 cg04180299 cg04188397 cg04197823 cg04199931 cg04199943 cg04206517 cg04209650 cg04216289 cg04219247 cg04219613 cg04220088 cg04220579 cg04227789 cg04232325 cg04234680 cg04235146 cg04243181 cg04245373 cg04258811 cg04261877 cg04262938 cg04269188 cg04271801 cg04274487 cg04278225 cg04281219 cg04282206 cg04283751 cg04285443 cg04292359 cg04297664 cg04307977 cg04309212 cg04315947 cg04317977 cg04319464 cg04321580 cg04322105 cg04324727 cg04342092 cg04343407 cg04352026 cg04352676 cg04356980 cg04360049 cg04361852 cg04362858 cg04366249 cg04370807 cg04371288 cg04378874 cg04380513 cg04385144 cg04385733 cg04389422 cg04389426 cg04391222 cg04396685 cg04399418 cg04401038 cg04401710 cg04413680 cg04417028 cg04424930 cg04430835 cg04435719 cg04437841 cg04438814 cg04439623 cg04449512 cg04450862 cg04454506 cg04457626 cg04461388 cg04468564 cg04480903 cg04487506 cg04489069 cg04493931 cg04494789 cg04501188 cg04504095 cg04513669 cg04515583 cg04516083 cg04524120 cg04524652 cg04525496 cg04534504 cg04539573 cg04539574 cg04543012 cg04547554 cg04547588 cg04554033 cg04555982 cg04557953 cg04559178 cg04566848 cg04568116 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cg25726789 cg25727572 cg25729826 cg25734420 cg25734490 cg25734572 cg25741578 cg25741731 cg25741953 cg25742540 cg25745651 cg25749267 cg25756166 cg25756435 cg25756853 cg25758217 cg25760229 cg25765619 cg25767504 cg25767985 cg25771677 cg25774276 cg25775832 cg25776555 cg25776856 cg25782003 cg25783173 cg25786436 cg25789216 cg25793521 cg25793630 cg25794153 cg25794402 cg25796631 cg25798122 cg25799020 cg25800082 cg25800170 cg25803642 cg25804018 cg25804470 cg25804860 cg25805709 cg25808577 cg25808892 cg25808906 cg25809635 cg25817261 cg25823419 cg25824543 cg25826913 cg25830048 cg25831204 cg25832771 cg25834869 cg25835225 cg25835669 cg25836094 cg25837710 cg25840208 cg25841943 cg25846285 cg25848557 cg25851842 cg25859141 cg25868286 cg25868769 cg25868998 cg25870025 cg25872752 cg25874034 cg25874421 cg25875316 cg25876509 cg25885108 cg25887236 cg25889711 cg25901204 cg25910314 cg25912580 cg25912827 cg25917510 cg25917621 cg25918303 cg25920483 cg25921512 cg25924096 cg25924911 cg25925764 cg25927164 cg25928603 cg25931385 cg25934680 cg25938977 cg25942031 cg25946952 cg25947544 cg25950369 cg25951582 cg25954223 cg25955837 cg25958857 cg25966893 cg25968571 cg25970618 cg25973895 cg25975626 cg25976563 cg25977958 cg25982140 cg25988717 cg25989216 cg25989719 cg25990647 cg25996614 cg25999722 cg26002713 cg26002784 cg26005082 cg26005761 cg26008007 cg26012941 cg26015176 cg26015401 cg26016484 cg26020635 cg26025543 cg26031047 cg26031255 cg26031541 cg26032101 cg26033293 cg26035323 cg26036993 cg26037945 cg26039848 cg26040332 cg26045220 cg26049726 cg26049744 cg26056104 cg26056348 cg26063563 cg26075208 cg26075747 cg26076412 cg26076750 cg26079699 cg26080444 cg26087678 cg26090855 cg26097011 cg26099766 cg26100711 cg26101410 cg26105156 cg26107850 cg26108678 cg26111157 cg26111761 cg26113636 cg26114642 cg26118408 cg26119367 cg26124242 cg26125811 cg26126052 cg26129310 cg26131754 cg26131803 cg26132084 cg26135012 cg26135506 cg26137971 cg26144437 cg26146542 cg26147351 cg26147657 cg26149167 cg26150071 cg26151087 cg26154342 cg26154670 cg26161849 cg26162932 cg26180126 cg26186613 cg26187876 cg26198463 cg26202915 cg26205131 cg26207201 cg26209058 cg26211360 cg26212496 cg26219095 cg26220298 cg26220773 cg26224223 cg26224915 cg26225694 cg26226802 cg26228351 cg26232715 cg26233374 cg26237168 cg26240185 cg26243894 cg26245302 cg26245667 cg26246411 cg26248878 cg26256158 cg26256916 cg26258845 cg26258944 cg26261793 cg26261973 cg26263773 cg26268636 cg26268866 cg26269703 cg26270362 cg26273150 cg26274596 cg26276294 cg26277730 cg26281453 cg26282655 cg26284638 cg26284982 cg26286805 cg26288577 cg26293015 cg26297688 cg26298979 cg26302094 cg26307820 cg26308113 cg26309111 cg26313152 cg26316082 cg26322231 cg26324132 cg26325209 cg26325806 cg26327071 cg26327118 cg26331625 cg26332534 cg26333513 cg26333822 cg26333837 cg26335281 cg26337020 cg26337123 cg26338195 cg26339484 cg26344233 cg26349375 cg26349672 cg26355004 cg26365299 cg26366048 cg26366107 cg26367591 cg26370608 cg26375461 cg26376168 cg26384031 cg26386846 cg26386852 cg26387473 cg26391350 cg26392989 cg26394825 cg26396617 cg26400840 cg26404669 cg26408235 cg26410121 cg26410483 cg26412722 cg26422059 cg26425669 cg26426142 cg26428136 cg26429856 cg26433722 cg26436315 cg26436330 cg26444995 cg26445292 cg26448406 cg26448489 cg26450740 cg26457761 cg26458288 cg26459419 cg26459700 cg26460816 cg26464411 cg26464889 cg26465214 cg26466323 cg26466856 cg26473651 cg26477844 cg26478074 cg26478485 cg26482665 cg26485174 cg26487082 cg26487948 cg26489994 cg26494225 cg26495865 cg26498020 cg26499055 cg26517151 cg26519745 cg26520120 cg26520722 cg26522240 cg26523670 cg26524541 cg26528620 cg26529911 cg26532253 cg26533949 cg26534508 cg26535805 cg26538529 cg26539524 cg26539593 cg26541920 cg26542888 cg26544277 cg26546557 cg26548288 cg26549326 cg26551211 cg26551897 cg26560928 cg26561148 cg26564040 cg26566415 cg26569144 cg26569469 cg26571942 cg26573321 cg26575690 cg26575738 cg26577201 cg26577454 cg26580421 cg26580801 cg26582768 cg26583041 cg26592281 cg26597539 cg26599209 cg26600608 cg26600954 cg26605406 cg26605467 cg26606184 cg26606256 cg26612362 cg26613140 cg26615127 cg26618965 cg26620450 cg26625629 cg26626525 cg26635208 cg26635845 cg26640467 cg26642510 cg26644059 cg26647219 cg26647312 cg26649005 cg26652447 cg26653400 cg26654790 cg26654994 cg26660414 cg26660801 cg26664090 cg26664797 cg26667720 cg26672104 cg26672688 cg26673436 cg26674800 cg26675876 cg26678852 cg26680047 cg26680520 cg26681211 cg26685735 cg26691604 cg26705425 cg26705553 cg26708548 cg26708817 cg26709356 cg26709950 cg26713507 cg26715571 cg26717983 cg26717995 cg26720389 cg26725153 cg26725274 cg26729197 cg26731119 cg26732691 cg26744375 cg26745332 cg26745551 cg26745770 cg26754826 cg26756506 cg26757053 cg26768712 cg26770917 cg26774079 cg26776069 cg26781150 cg26784596 cg26785250 cg26785617 cg26788916 cg26789332 cg26789732 cg26789869 cg26790372 cg26791905 cg26792080 cg26792116 cg26792755 cg26795730 cg26796283 cg26797372 cg26797585 cg26798879 cg26799802 cg26800162 cg26800371 cg26802053 cg26802291 cg26815291 cg26820118 cg26822161 cg26823584 cg26824467 cg26825751 cg26825934 cg26826871 cg26831241 cg26832509 cg26834887 cg26838995 cg26839117 cg26840598 cg26843430 cg26847805 cg26851796 cg26853987 cg26857911 cg26859900 cg26861593 cg26862527 cg26864834 cg26870192 cg26872138 cg26873311 cg26877715 cg26878816 cg26881527 cg26886307 cg26886972 cg26887632 cg26891924 cg26892444 cg26896668 cg26899113 cg26904700 cg26904914 cg26908611 cg26908755 cg26909954 cg26910092 cg26912242 cg26912984 cg26915774 cg26917673 cg26918442 cg26919818 cg26922444 cg26923908 cg26924294 cg26928603 cg26929012 cg26929348 cg26932552 cg26933063 cg26942943 cg26943708 cg26948603 cg26953462 cg26954625 cg26955196 cg26957677 cg26958597 cg26958806 cg26963029 cg26966630 cg26966707 cg26967167 cg26967579 cg26968387 cg26970841 cg26970847 cg26972058 cg26973488 cg26982364 cg26984626 cg26986911 cg26987855 cg26988146 cg26988215 cg26988406 cg26988692 cg26988895 cg26990660 cg26992213 cg26995744 cg26995992 cg26998900 cg26999505 cg27002185 cg27003849 cg27004639 cg27009208 cg27011620 cg27014927 cg27015174 cg27016990 cg27018185 cg27023953 cg27025752 cg27028168 cg27029179 cg27030612 cg27031632 cg27032142 cg27036581 cg27037103 cg27040423 cg27041875 cg27042000 cg27046492 cg27051954 cg27052403 cg27056599 cg27060622 cg27061115 cg27062573 cg27062617 cg27063138 cg27064266 cg27066284 cg27068170 cg27068490 cg27070869 cg27072012 cg27072749 cg27073079 cg27078812 cg27080194 cg27080211 cg27082486 cg27084026 cg27084903 cg27085904 cg27086020 cg27086773 cg27087057 cg27088830 cg27090492 cg27093143 cg27099166 cg27099274 cg27099500 cg27101125 cg27108362 cg27109877 cg27111970 cg27112897 cg27116061 cg27116819 cg27116888 cg27117639 cg27121584 cg27123533 cg27123665 cg27125972 cg27128984 cg27133864 cg27134223 cg27136241 cg27138195 cg27139933 cg27140220 cg27143938 cg27147871 cg27151812 cg27154391 cg27162464 cg27164044 cg27166527 cg27173322 cg27175294 cg27178940 cg27180880 cg27182172 cg27192597 cg27194921 cg27197380 cg27199872 cg27204739 cg27209110 cg27212234 cg27213352 cg27220968 cg27222172 cg27223727 cg27225570 cg27229407 cg27230038 cg27233989 cg27240775 cg27242945 cg27243389 cg27244585 cg27249178 cg27250841 cg27253670 cg27254295 cg27257566 cg27258025 cg27258933 cg27266382 cg27269940 cg27275523 cg27276115 cg27279904 cg27286572 cg27293549 cg27295197 cg27295781 cg27299538 cg27303430 cg27304043 cg27304516 cg27306119 cg27307206 cg27308557 cg27310163 cg27311227 cg27313572 cg27315243 cg27319192 cg27323154 cg27326642 cg27326687 cg27326823 cg27327588 cg27331401 cg27333693 cg27335720 cg27336178 cg27338377 cg27339550 cg27340350 cg27346528 cg27347140 cg27351780 cg27352765 cg27355739 cg27358097 cg27359731 cg27360003 cg27361727 cg27362103 cg27362222 cg27363741 cg27364874 cg27371984 cg27372898 cg27375072 cg27380218 cg27380819 cg27382164 cg27383277 cg27386292 cg27388983 cg27391396 cg27392850 cg27395666 cg27395939 cg27405644 cg27405960 cg27410952 cg27412987 cg27413430 cg27417717 cg27418586 cg27420520 cg27421939 cg27422496 cg27425719 cg27425996 cg27430961 cg27432847 cg27433451 cg27434954 cg27434993 cg27436264 cg27437806 cg27443310 cg27445400 cg27447053 cg27447689 cg27447868 cg27448015 cg27449131 cg27449352 cg27451672 cg27453606 cg27457941 cg27462418 cg27464311 cg27465275 cg27465717 cg27466237 cg27469783 cg27475076 cg27476262 cg27476576 cg27478700 cg27479418 cg27482605 cg27486637 cg27487839 cg27492749 cg27496650 cg27502066 cg27504805 cg27506082 cg27506462 cg27507700 cg27511208 cg27511255 cg27517702 cg27519691 cg27525037 cg27530629 cg27533019 cg27534281 cg27536453 cg27538026 cg27545919 cg27546065 cg27546736 cg27546949 cg27549834 cg27552287 cg27555382 cg27558479 cg27558594 cg27559724 cg27560922 cg27564875 cg27565366 cg27565555 cg27569040 cg27569822 cg27576755 cg27577527 cg27579532 cg27584828 cg27586581 cg27586588 cg27591375 cg27594756 cg27597110 cg27599958 cg27604626 cg27606464 cg27607338 cg27612364 cg27619163 cg27627570 cg27629771 cg27632050 cg27633530 cg27635394 cg27636310 cg27637930 cg27637948 cg27638597 cg27641141 cg27646469 cg27647384 cg27650778 cg27654641 cg27655921 cg27658416 cg27659841 cg27665925

TABLE II CRC Panel cg20295442 cg20463526 cg26122980 cg09822538 cg26212877 cg20560075 cg16601494 cg27555582 cg10864878 cg03384825 cg22538054 cg11017065 cg13405887 cg09022943 cg19651223 cg16476975 cg17228900 cg05527869 cg01051310 cg12348588 cg20329153 cg02970696 cg02259324 cg15778437 cg07703462 cg25088758 cg04537567 cg17222500 cg15490715 cg20219457 cg16300300 cg11979589 cg05051043 cg12940822 cg01563031 cg22065614 cg13024709 cg15467646 cg18120376 cg19939997 cg19824907 cg18683604 cg07188591 cg14175690 cg15658945 cg01938650 cg20556517 cg13726682 cg06952671 cg06913330 cg22834653 cg05046525 cg17035091 cg03419885 cg10512745 cg03976877 cg09667303 cg03401096 cg01883425 cg17287235 cg02173749 cg11501438 cg04897742 cg14236735 cg11855526 cg17768491 cg09498146 cg25730685 cg10236452 cg04184836 cg04198308 cg10362542 cg14348439 cg17470837 cg11281641 cg17698295 cg11666087 cg18587340 cg25798987 cg07976064 cg13101087 cg09975620 cg23217126 cg10457056 cg22623967 cg08430489 cg09740671 cg02043600 cg24392818 cg25975712 cg03225817 cg26820055 cg18638914 cg00421139 cg21672843 cg15384598 cg18884037 cg01419567 cg13554086 cg07974511 cg07700514 cg23272632 cg16993043 cg01394819 cg23300368 cg16556906 cg12816961 cg01947130 cg02604524 cg24487076 cg06528267 cg21938148 cg03356747 cg16334314 cg20864608 cg03640756 cg13223402 cg04125371 cg05209770 cg00843236 cg00662647 cg20079899 cg17029156 cg08558397 cg08452658 cg01261798 cg04904331 cg03571927 cg08189989 cg15699267 cg04790084 cg10058779 cg16918905 cg27200446 cg15015920 cg22879515 cg16638385 cg02511156 cg02455397 cg27442308 cg20631014 cg00817367 cg22474464 cg09802835 cg22871668 cg19875368 cg14098681 cg15779837 cg08354093 cg14794428 cg15825786 cg12417685 cg04272632 cg21039708 cg24033330 cg14485004 cg13690864 cg20012008 cg03133266 cg26274580 cg20686234 cg03957481 cg04718428 cg14473327 cg15207742 cg12907379 cg12042659 cg05374412 cg16676492 cg03755177 cg21314480 cg16230141 cg10453425 cg26495865 cg05522774 cg10293925 cg10002178 cg02583633 cg02539855 cg20443778 cg25012919 cg18786873 cg15461516 cg13867865 cg09239744 cg09155997 cg09462445 cg14648916 cg13557668 cg09461837 cg14936269 cg23697417 cg05171952 cg14409941 cg11428724 cg23932491 cg05344430 cg19497031 cg16520288 cg09495977 cg14568217 cg21329599 cg27111463 cg05758094 cg21875802 cg18355902 cg06997381 cg08434234 cg19178853 cg07017374 cg02842227 cg15424739 cg21176643 cg23215729 cg10096161 cg02483484 cg11859584 cg02174225 cg06651311 cg20450979 cg06266613 cg15286044 cg17771605 cg09683824 cg16899920 cg07821427 cg12859211 cg12686317 cg00625334 cg22284043 cg01878345 cg26990102 cg24686074 cg16332256 cg04453180 cg24521633 cg16584573 cg05178576 cg22878622 cg16729832 cg27264249 cg19752627 cg24773418 cg01419831 cg18646207 cg16514543 cg18762727 cg03257575 cg13776340 cg16474297 cg03698948 cg02058731 cg16482474 cg27364741 cg13562911 cg24305584 cg15261247 cg26365854 cg11878331 cg04058593 cg18607529 cg06630204 cg27101125 cg14725151 cg18759960 cg07057177 cg26615127 cg07068756 cg11253514 cg24886267 cg27317433 cg24262066 cg18623980 cg11677857 cg02869459 cg13619824 cg22138430 cg14657517 cg01579950 cg06172475 cg16307705 cg23201032 cg14535068 cg07752026 cg24403845 cg01501819 cg27493301 cg00114029 cg26739280 cg26818735 cg21901946 cg19320476 cg26684946 cg23359394 cg27510832 cg00100121 cg26834169 cg00017221 cg06319822 cg15409931 cg24876960 cg07078225 cg05562381 cg04156369 cg07060006 cg16485558 cg07495363 cg17386213 cg07283152 cg11689407 cg13432708 cg24599249 cg25767985 cg21678377 cg13464448 cg18406197 cg11107669 cg16366473 cg07628404 cg14256587 cg14667871 cg23719318 cg11732619 cg11821817 cg14965220 cg05228284 cg04171539 cg13368519 cg07627556 cg20593611 cg17847723 cg11881754 cg06393563 cg19769760 cg17483297 cg23978504 cg19924619 cg17263061 cg04100696 cg13652513 cg12865552 cg26156687 cg07283114 cg02966153 cg09912350 cg20665002 cg08157228 cg26232818 cg21583226 cg15344220 cg08460041 cg21325154 cg14218042 cg03142956 cg13670601 cg05332960 cg26892444 cg25184481 cg04689080 cg24134479 cg26547924 cg23462956 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The selection of a hypermethylated site according to the present method is defined as follows. For each clinical sample of a specific cancer type in a database, e.g., The Cancer Genome Atlas, the methylation level is determined for each methylation site i from a starting set of sites as described in the preceding paragraph. For instance, for each clinical sample from a set of colon adenocarcinoma samples in The Cancer Genome Atlas, the methylation state at each of the CpG sites listed in Tables I and II is determined, and the mean methylation level at each site i calculated as described elsewhere in this application. In some embodiments, the methylation level can be determined as the fraction of ‘C’ bases out of ‘C′+′U’ total bases at a target CpG site i following the bisulfite treatment. In other embodiments, the methylation level can be determined as the fraction of ‘C’ bases out of ‘C′+′T’ total bases at site i following the bisulfite treatment and subsequent nucleic acid amplification. The mean methylation level at each site is then evaluated to determine if one or more threshold is met. In some embodiments, a threshold selects those sites having the highest-ranked mean methylation values for a specific cancer type. For example, the threshold can be those sites having a mean methylation level that is the top 50%, the top 40%, the top 30%, the top 20%, the top 10%, the top 5%, the top 4%, the top 3%, the top 2%, or the top 1% of mean methylation levels across all sites i tested for a specific cancer type, e.g., colon adenocarcinoma. Alternatively, the threshold can be those sites having a mean methylation level that is at a percentile rank greater than or equivalent to 50, 60, 70, 80, 90, 95, 96, 97, 98, or 99. In other embodiments, a threshold can be based on the absolute value of the mean methylation level. For instance, the threshold can be those sites having a mean methylation level that is greater than 99%, greater than 98%, greater than 97%, greater than 96%, greater than 95%, greater than 90%, greater than 80%, greater than 70%, greater than 60%, greater than 50%, greater than 40%, greater than 30%, greater than 20%, greater than 10%, greater than 9%, greater than 8%, greater than 7%, greater than 6%, greater than 5%, greater than 4%, greater than 3%, or greater than 2%. The relative and absolute thresholds can be applied to the mean methylation level at each site i individually or in combination. As an illustration of a combined threshold application, one may select a subset of sites that are in the top 3% of all sites tested by mean methylation level and also have an absolute mean methylation level of greater than 6%. The result of this selection process is a plurality of lists, one for each cancer type, of specific hypermethylated sites (e.g., CpG sites) that are considered the most informative for that cancer type. These lists are then used to identify or classify a test genomic DNA sample from a test organism, i.e. to determine whether the test organism has a specific cancer type.

In the next step of the present method, a test genomic DNA sample from a test organism is analyzed by determining the methylation levels at each site i on the list of hypermethylated sites for each cancer type, and these methylation levels for each site are then averaged to calculate the average methylation level across the hypermethylated sites for each cancer type. For instance, for each hypermethylated site i for colon adenocarcinoma, the methylation level at each site i on the list of hypermethylated sites for colon adenocarcinoma is determined, and these methylation levels are then averaged to provide a single average methylation level. This process is repeated using the previously defined lists of hypermethylated sites for each of the cancer types, and results in a set of average methylation levels, each corresponding to a different cancer type. The average methylation levels are then ranked from lowest to highest. The cancer type corresponding to the highest average methylation level is considered to be associated with the test genomic DNA, i.e. the cancer type is deemed to be present in the test organism. It is understood that the normalized methylation difference or z-score also can be used in the present method instead of the methylation level at each CpG site.

For cancer screening or detection, the determination of a methylation level of a plasma (or other biologic) sample can be used in conjunction with other modalities for cancer screening or detection such as prostate specific antigen measurement (e.g. for prostate cancer), carcinoembryonic antigen (e.g. for colorectal carcinoma, gastric carcinoma, pancreatic carcinoma, lung carcinoma, breast carcinoma, medullary thyroid carcinoma), alpha fetoprotein (e.g. for liver cancer or germ cell tumors) and CA19-9 (e.g. for pancreatic carcinoma).

Useful methylation sites that can be detected in a method set forth herein, for example, to evaluate cancer are include those present in the Cancer Genome Atlas (see, for example, Cancer Genome Atlas Research Network et al., Nature Genetics 45:1113-1120 (2013)) or the selected CpG sites of the Pan Cancer Panel set forth in Table I (the listed methylation sites are from Genome Build 37). Further examples of CpG sites that can be useful, for example, to identify or monitor colorectal cancer, are described in Worthley et al. Oncogene 29, 1653-1662 (2010) or set forth in Table II (the listed methylation sites are from Genome Build 37). Useful methylation markers for detection of ovarian cancer are set forth in US Pat. App. Pub. No. 2008/0166728 A1, which is incorporated herein by reference. All or a subset of the markers set forth herein and/or listed in a reference above can be used in a method set forth herein. For example, at least 10, 25, 50, 100, 1×10³, 1×10⁴ or more of the markers can be used.

Analysis of the methylation, prognosis or diagnosis information derived from a method set forth herein can conveniently be performed using various computer executed algorithms and programs. Therefore, certain embodiments employ processes involving data stored in or transferred through one or more computer systems or other processing systems. Embodiments of the invention also relate to apparatus for performing these operations. This apparatus may be specially constructed for the required purposes, or it may be a general-purpose computer (or a group of computers) selectively activated or reconfigured by a computer program and/or data structure stored in the computer. In some embodiments, a group of processors performs some or all of the recited analytical operations collaboratively (e.g., via a network or cloud computing) and/or in parallel. A processor or group of processors for performing the methods described herein may be of various types including microcontrollers and microprocessors such as programmable devices (e.g., CPLDs and FPGAs) and non-programmable devices such as gate array ASICs or general purpose microprocessors.

In addition, certain embodiments relate to tangible and/or non-transitory computer readable media or computer program products that include program instructions and/or data (including data structures) for performing various computer-implemented operations. Examples of computer-readable media include, but are not limited to, semiconductor memory devices, magnetic media such as disk drives, magnetic tape, optical media such as CDs, magneto-optical media, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM) and random access memory (RAM). The computer readable media may be directly controlled by an end user or the media may be indirectly controlled by the end user. Examples of directly controlled media include the media located at a user facility and/or media that are not shared with other entities. Examples of indirectly controlled media include media that is indirectly accessible to the user via an external network and/or via a service providing shared resources such as a “cloud.” A particularly useful cloud is one that is configured and administered to store and analyze genetic data such as the BaseSpace™ service (Illumina, Inc. San Diego Calif.), or cloud services described in US Pat. App. Pub. Nos. 2013/0275486 A1 or 2014/0214579 A1 (each of which is incorporated herein by reference). Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter.

In some embodiments, the data or information employed in the disclosed methods and apparatus is provided in an electronic format. Such data or information may include reads derived from a nucleic acid sample, reference sequences, methylation states, patterns of methylation states, methylation difference scores, normalized methylation difference scores, aggregate coverage-weighted normalized methylation difference scores, methylation scores, coverage-weighted methylation scores, counseling recommendations, diagnoses, and the like. As used herein, data or other information provided in electronic format is available for storage on a machine and transmission between machines. Conventionally, data in electronic format is provided digitally and may be stored as bits and/or bytes in various data structures, lists, databases, etc. The data may be embodied electronically, optically, etc.

In addition, certain embodiments relate to tangible and/or non-transitory computer readable media or computer program products that include instructions and/or data (including data structures) for performing various computer-implemented operations. One or more of the steps of a method set forth herein can be carried out by a computer program that is present in tangible and/or non-transitory computer readable media, or carried out using computer hardware.

For example, a computer program product is provided and it comprises a non-transitory computer readable medium on which is provided program instructions for steps of (a) obtaining a test data set that includes (i) methylation states for a plurality of sites from test genomic DNA from at least one test organism, and (ii) coverage at each of the sites for detection of the methylation states; (b) obtaining methylation states for the plurality of sites in reference genomic DNA from one or more reference individual organisms, (c) determining, for each of the sites, the methylation difference between the test genomic DNA and the reference genomic DNA, thereby providing a normalized methylation difference for each site; (d) weighting the normalized methylation difference for each site by the coverage at each of the sites, thereby determining an aggregate coverage-weighted normalized methylation difference score, and (e) storing or transmitting the aggregate coverage-weighted normalized methylation difference score.

Methods disclosed herein can also be performed using a computer processing system which is adapted or configured to perform a method for identifying methylation states or other characteristics of nucleic acids. Thus, in one embodiment, the invention provides a computer processing system which is adapted or configured to perform a method as described herein. In one embodiment, the apparatus comprises a nucleic acid detection device, such as a nucleic acid sequencing device, adapted or configured to determine methylation states and/or other characteristics of nucleic acids. The apparatus may also include components for processing a sample from a test organism and/or reference organism. Such components are described elsewhere herein.

Nucleic acid sequence, methylation state, methylation pattern, or other data, can be input into a computer or stored on a computer readable medium either directly or indirectly. In one embodiment, a computer system is directly coupled to a nucleic acid detection device (e.g. sequencing device) that determines methylation states of nucleic acids from samples. Data or other information from such tools are provided via interface in the computer system. Alternatively, the methylation data processed by systems are provided from a data storage source such as a database or other repository. Once available to the processing apparatus, a memory device or mass storage device buffers or stores, at least temporarily, methylation states or other characteristics of the nucleic acids. In addition, the memory device may store methylation differences, normalized methylation differences, aggregate weighted normalized methylation differences, methylation scores, or coverage-weighted methylation scores as described herein. The memory may also store various routines and/or programs for analyzing or presenting such information. Such programs/routines may include programs for performing statistical analyses, etc.

In one example, a user provides a sample to a nucleic acid sequencing apparatus. Data is collected and/or analyzed by the sequencing apparatus which is connected to a computer. Software on the computer allows for data collection and/or analysis. Data can be stored, displayed (e.g. via a monitor or other similar device), and/or sent to another location. The computer may be connected to the internet which is used to transmit data to a handheld device and/or cloud environment utilized by a remote user (e.g., a physician, scientist or analyst). It is understood that the data can be stored and/or analyzed prior to transmittal. In some embodiments, raw data is collected and sent to a remote user or apparatus that will analyze and/or store the data. Transmittal can occur via the internet, but can also occur via satellite or other connection. Alternately, data can be stored on a computer-readable medium and the medium can be shipped to an end user (e.g., via mail). The remote user can be in the same or a different geographical location including, but not limited to, a building, city, state, country or continent.

In some embodiments, the methods also include collecting data regarding a plurality of polynucleotide sequences (e.g., reads, tags and/or methylation states) and sending the data to a computer or other computational system. For example, the computer can be connected to laboratory equipment, e.g., a sample collection apparatus, a nucleotide amplification apparatus, a nucleotide sequencing apparatus, or a hybridization apparatus. The computer can then collect applicable data gathered by the laboratory device. The data can be stored on a computer at any step, e.g., while collected in real time, prior to the sending, during or in conjunction with the sending, or following the sending. The data can be stored on a computer-readable medium that can be extracted from the computer. The data that has been collected or stored can be transmitted from the computer to a remote location, e.g., via a local network or a wide area network such as the internet. At the remote location various operations can be performed on the transmitted data as described below.

Among the types of electronically formatted data that may be stored, transmitted, analyzed, and/or manipulated in systems, apparatus, and methods disclosed herein are the following: reads obtained by sequencing nucleic acids in a test sample, methylation states for sites in the nucleic acids, one or more reference genome or sequence, methylation difference score, normalized methylation difference score, aggregate coverage-weighted normalized methylation difference score, methylation score, or coverage-weighted methylation score as described herein.

These various types of data may be obtained, stored, transmitted, analyzed, and/or manipulated at one or more locations using distinct apparatus. The processing options span a wide spectrum. Toward one end of the spectrum, all or much of this information is stored and used at the location where the test sample is processed, e.g., a doctor's office or other clinical setting. Toward another extreme, the sample is obtained at one location, it is processed (e.g. prepared, detected or sequenced) at a second location, data is analyzed (e.g. sequencing reads are aligned) and methylation characteristics are determined at a third location (or several locations), and diagnoses, recommendations, and/or plans are prepared at a fourth location (or the location where the sample was obtained).

In various embodiments, the methylation data are generated on a nucleic acid detection apparatus (e.g. sequencing apparatus) and then transmitted to a remote site where they are processed to determine methylation characteristics. At this remote location, as an example, methylation difference score, normalized methylation difference score, aggregate coverage-weighted normalized methylation difference score, methylation score, or coverage-weighted methylation score can be determined. Also at the remote location, the methylation characteristics can be evaluated to make a prognostic or diagnostic determination.

Any one or more of these operations may be automated as described elsewhere herein. Typically, the detection of nucleic acids and the analyzing of sequence data will be performed computationally. The other operations may be performed manually or automatically.

Examples of locations where sample collection may be performed include health practitioners' offices, clinics, patients' homes (where a sample collection tool or kit is provided), and mobile health care vehicles. Examples of locations where sample processing prior to methylation detection may be performed include health practitioners' offices, clinics, patients' homes (where a sample processing apparatus or kit is provided), mobile health care vehicles, and facilities of nucleic acid analysis providers. Examples of locations where nucleic acid detection (e.g. sequencing) may be performed include health practitioners' offices, clinics, health practitioners' offices, clinics, patients' homes (where a sample sequencing apparatus and/or kit is provided), mobile health care vehicles, and facilities of nucleic acid analysis providers. The location where the nucleic acid detection takes place may be provided with a dedicated network connection for transmitting sequence data (typically reads) in an electronic format. Such connection may be wired or wireless and may be configured to send the data to a site where the data can be processed and/or aggregated prior to transmission to a processing site. Data aggregators can be maintained by health organizations such as Health Maintenance Organizations (HMOs).

The analyzing operations may be performed at any of the foregoing locations or alternatively at a further remote site dedicated to computation and/or the service of analyzing nucleic acid sequence data. Such locations include for example, clusters such as general purpose server farms, the facilities of a genetic analysis service business, and the like. In some embodiments, the computational apparatus employed to perform the analysis is leased or rented. The computational resources may be part of an internet accessible collection of processors such as processing resources colloquially known as the “cloud”, examples of which are provided elsewhere herein. In some cases, the computations are performed by a parallel or massively parallel group of processors that are affiliated or unaffiliated with one another. The processing may be accomplished using distributed processing such as cluster computing, grid computing, and the like. In such embodiments, a cluster or grid of computational resources collective form a super virtual computer composed of multiple processors or computers acting together to perform the analysis and/or derivation described herein. These technologies as well as more conventional supercomputers may be employed to process sequence data as described herein. Each is a form of parallel computing that relies on processors or computers. In the case of grid computing these processors (often whole computers) are connected by a network (private, public, or the Internet) by a conventional network protocol such as Ethernet. By contrast, a supercomputer has many processors connected by a local high-speed computer bus.

In certain embodiments, the diagnosis (e.g., determination that the patient has a particular type of cancer) is generated at the same location as the analyzing operation. In other embodiments, it is performed at a different location. In some examples, reporting the diagnosis is performed at the location where the sample was taken, although this need not be the case. Examples of locations where the diagnosis can be generated or reported and/or where developing a plan is performed include health practitioners' offices, clinics, internet sites accessible by computers, and handheld devices such as cell phones, tablets, smart phones, etc. having a wired or wireless connection to a network. Examples of locations where counseling is performed include health practitioners' offices, clinics, Internet sites accessible by computers, handheld devices, etc.

In some embodiments, the sample collection, sample processing, and methylation state detection operations are performed at a first location and the analyzing and deriving operation is performed at a second location. However, in some cases, the sample collection is collected at one location (e.g., a health practitioner's office or clinic) and the sample processing and methylation state detecting is performed at a different location that is optionally the same location where the analyzing and deriving take place.

In various embodiments, a sequence of the above-listed operations may be triggered by a user or entity initiating sample collection, sample processing and/or methylation state detection. After one or more of these operations have begun execution the other operations may naturally follow. For example, a nucleic acid sequencing operation may cause reads to be automatically collected and sent to a processing apparatus which then conducts, often automatically and possibly without further user intervention, the methylation state analysis and determination of methylation difference score, normalized methylation difference score, aggregate coverage-weighted normalized methylation difference score, methylation score, or coverage-weighted methylation score. In some implementations, the result of this processing operation is then automatically delivered, possibly with reformatting as a diagnosis, to a system component or entity that processes or reports the information to a health professional and/or patient. As explained, such information can also be automatically processed to produce a treatment, testing, and/or monitoring plan, possibly along with counseling information. Thus, initiating an early stage operation can trigger an end to end process in which the health professional, patient or other concerned party is provided with a diagnosis, a plan, counseling and/or other information useful for acting on a physical condition. This is accomplished even though parts of the overall system are physically separated and possibly remote from the location of, e.g., the sample collection and nucleic acid detection apparatus.

In some embodiments the results of a method set forth herein will be communicated to an individual by a genetic counselor, physician (e.g., primary physician, obstetrician, etc.), or other qualified medical professional. In certain embodiments the counseling is provided face-to-face, however, it is recognized that in certain instances, the counseling can be provided through remote access (e.g., via text, cell phone, cell phone app, tablet app, internet, and the like).

In some embodiments, disclosure of results to a medical professional or to a patient can be delivered by a computer system. For example, “smart advice” systems can be provided that in response to test results, instructions from a medical care provider, and/or in response to queries (e.g., from a patient) provide genetic counseling information. In certain embodiments the information will be specific to clinical information provided by the physician, healthcare system, and/or patient. In certain embodiments the information can be provided in an iterative manner. Thus, for example, the patient can provide “what if” inquiries and the system can return information such as diagnostic options, risk factors, timing, and implication of various outcomes.

In particular embodiments, the results or other information generated in a method set forth herein can be provided in a transitory manner (e.g., presented on a computer screen). In certain embodiments, the information can be provided in a non-transitory manner. Thus, for example, the information can be printed out (e.g., as a list of options and/or recommendations optionally with associated timing, etc.) and/or stored on computer readable media (e.g., magnetic media such as a local hard drive, a server, etc., optical media, flash memory, and the like).

It will be appreciated that typically such systems will be configured to provide adequate security such that patient privacy is maintained, e.g., according to prevailing standards in the medical field.

The foregoing discussion of genetic counseling is intended to be illustrative and not limiting. Genetic counseling is a well-established branch of medical science and incorporation of a counseling component with respect to the methods described herein is within the scope and skill of the practitioner. Moreover, it is recognized that as the field progresses, the nature of genetic counseling and associated information and recommendations is likely to alter.

Example I Analytical Sensitivity of ctDNA Methylation-Based Cancer Detection Using Aggregate Normalized Coverage-Weighted Methylation Differences

This example describes a highly sensitive assay for detecting methylation in circulating tumor DNA (ctDNA). Aberrant DNA methylation is a widespread phenomenon in cancer and may be among the earliest changes to occur during oncogenesis. The assay described in this example can be useful for cancer screening.

The general approach applied here includes targeted methylation sequencing for multiple CpG sites affected in cancer.

Technical challenges addressed by the approach include providing ultra-high sensitivity and specificity that benefits screening applications, providing a protocol for targeted methyl-seq from low input ctDNA, and providing bioinformatics algorithms for analysis of methylation levels across a large number of targeted sites.

Targeted Capture Probe Design

Two targeted methylation panels were developed. The Pan-Cancer Panel targets 9,921 affected CpG sites in 20 major cancer types as selected from The Cancer Genome Atlas Database. The CpG sites included in the Pan-Cancer Panel are listed in Table I. The CRC Panel targets 1,162 affected CpG sites in colorectal cancer. The CpG sites included in the CRC Panel are listed in Table II. The CpG sites listed in Table I and Table II refer to Genome Build 37.

The probe sequences for the CpG sites were selected from the Infinium HM450 array (Illumina, Inc., San Diego, Calif.). Design principles for the probes are shown in FIG. 1. Two probes were used for targets having greater than 4 CpG sites, including a completely methylated probe (having a G nucleotides that complements the C position of each CpG site) and completely unmethylated probe (having an A nucleotide that complements the U that is expected to result from bisulfite conversion of each of the C positions of a CpG site) as shown in FIG. 1. In contrast, only one probe was used for targets having 4 or fewer CpG sites (the probe includes degenerate nucleotide R, complementary to U or C, at the C position of each CpG site).

Isolation and Extraction of cfDNA from Plasma

Plasma samples were obtained from human blood draws. Cell free DNA (cfDNA) was extracted using the QIAamp Circulating Nucleic Acid Kit (Qiagen, Hilden, Germany). Targeted ctDNA methylation sequencing was carried out according to the workflow shown in FIG. 2, and as set forth below in the context of evaluating titration and detection sensitivity.

Titration and Detection Sensitivity

NA12878 genomic DNA was purchased from Coriell Institute (Coriell Institute, Camden, N.J.), and LS1034 genomic DNA was purchased from ATCC (ATCC, Manassas, Va.). Genomic DNA was fragmented using Covaris M200 (Covaris, Woburn, Mass.) and size-selected to 130-250 bp using BluePippin (Sage Science, Beverly, Mass.) to simulate the size distribution of cfDNA. DNA quantification was performance using Quant-iT™ PicoGreen® dsDNA Assay Kit (ThermoFisher Scientific, Grand Island, N.Y.). 10%, 1%, or 0.1% LS1034 DNA was spiked into NA12878 DNA background to make the DNA mixtures. 30 ng of each mixture, 100% NA12878, or 100% LS1034 DNA was used in library preparation. Three replicated libraries were generated for each titration level. A set of six replicates of NA12878 was used as the baseline reference genome.

Extracted cfDNA or sheared and size-selected genomic DNA was bisulfite treated and purified using EZ DNA Methylation-Lightning Kit (Zymo Research, Irvine, Calif.).

Bisulfite-seq Libraries were prepared using the Accel-NGS® Methyl-Seq DNA Library Kit (Swift Biosciences, Ann Arbor, Mich.).

Targeted capture was carried out on the bisulfite-seq libraries using probes that were complementary to fragments having the CpG sites listed in Table I or Table II. Capture probes were synthesized and biotinylated at Illumina, Inc. Target capture was performed using Illumina TruSight™ Rapid Capture Kit according to manufacturer's instructions except that customized capture probes were used, and hybridization and wash steps were performed at 48 C.

The products of the capture step were sequenced on an Illumina HiSeq 2500 Sequencer using 2×100 cycle runs, with four samples in rapid run mode, according to manufacturer's instructions.

Bioinformatic Analysis

FASTQ sequences were demultiplexed followed by in silico demethylation whereby all C's on read 1 were converted to T's and all G's on read 2 were converted to A's. Subsequently, these “demethylated” FASTQ sequences were aligned using BWA (v 0.7.10-r789) to an index comprising a “demethylated” hg19 genome. BWA alignment is described in Li and Durbin (2010) Fast and accurate long-read alignment with Burrows-Wheeler Transform. Bioinformatics, Epub. [PMID: 20080505], which is incorporated herein by reference. Following alignment, the “demethylated” FASTQ sequences were replaced with the original FASTQ sequences. Methylation levels were calculated as the fraction of ‘C’ bases at a target CpG site out of ‘C’+‘T’ total bases.

Following calculation of methylation levels at each CpG site for each sample and replicate, aggregate coverage-weighted normalized methylation difference z-scores were calculated as follows.

(1) the methylation level at each CpG site was normalized by subtracting the mean methylation level in baseline and dividing by the standard deviation of methylation levels in baseline to obtain a per-site z-score. Specifically, the normalized methylation difference at each CpG site was determined according to the formula:

$Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$

where Z_(i) represents a normalized methylation difference for a particular site identified as i, χ_(i) represents the methylation level at site i in the test genomic DNA, μ_(i) represents the mean methylation level at site i in the reference genome, and σ_(i) represents the standard deviation of methylation levels at site i in the reference genomic DNA.

(2) the z-score at each CpG site was multiplied by the coverage observed at the CpG site, and the coverage-weighted z-score was then summed across all CpG sites and then divided by the sum of the coverage squared at each CpG site. More specifically, an aggregate coverage-weighted methylation difference z-score (an example of an aggregate coverage-weighted normalized methylation difference score, A) was determined according to the formula:

$A = \frac{\sum_{i = 1}^{k}\; {w_{i}Z_{i}}}{\sqrt{\sum_{i = 1}^{k}\; w_{i}^{2}}}$

where w_(i) represents the coverage at site i and k represents the total number of sites.

Results

A titration experiment was performed to demonstrate analytical sensitivity using a colorectal cancer cell line LS1034 and a normal cell line NA12878. Namely, targeted ctDNA methylation sequencing was performed in triplicates using both the Pan-Cancer and CRC panels on 0.1%, 1%, and 10% titrations of LS1034 into NA12878 along with pure LS1034 and pure NA12878. For each of the 15 sample replicates, the aggregate coverage-weighted methylation difference z-scores were calculated using the normal NA12878 samples as the baseline (FIG. 3). The results indicate that the within sample variation is far less than the variation between titration levels. In particular, the clear separation of the 0.1% titration of LS1034 into NA12878 from the NA12878 sample indicates the assay and accompanying aggregate coverage-weighted methylation difference z-score can achieve a 0.1% limit of detection.

Results obtained using the methods of this example provide high sensitivity evaluation of the cumulative effect of multiple affected CpG sites across the genome. By providing a method for detecting methylation patterns the methods of this example can provide improved cancer diagnosis than methods that rely on detection of somatic mutations, as evidenced by the improved concordance in alternations between CRC tissue and corresponding plasma when evaluating DNA methylation markers compared to somatic mutations (see, for example, Danese et al., “Comparison of Genetic and Epigenetic Alterations of Primary Tumors and Matched Plasma Samples in Patients with Colorectal Cancer” PLoS ONE 10(5):e0126417. doi:10.1371/journal.pone.0126417 (2015), which is incorporated herein by reference). The methods described in this example also provide identification of tissue origin for cancer. Specifically, tissue specific methylation markers have been shown to be useful to trace the tissue origin of particular ctDNA sequences (see, for example, Sun et al. “Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments” Proc. Natl. Acad. Sci, USA 112 (40) E5503-E5512 (2015), which is incorporated herein by reference).

Example II Analytical Sensitivity of ctDNA Methylation-Based Cancer Detection Using Coverage-Weighted Methylation Scores

This example describes an alternative highly sensitive assay for detecting methylation in circulating tumor DNA (ctDNA). The assay described in this example also can be useful for cancer screening, monitoring disease progression, or evaluating a patient's response to a therapeutic treatment.

Targeted Capture Probe Design

For this study, the two targeted methylation panels described in Example I were pooled together. The Pan-Cancer Panel targets 9,921 affected CpG sites in 20 major cancer types as selected from The Cancer Genome Atlas Database. The CpG sites included in the Pan-Cancer Panel are listed in Table I. The CRC Panel targets 1,162 affected CpG sites in colorectal cancer. The CpG sites included in the CRC Panel are listed in Table II. The combined CpG sites listed in Table I and Table II refer to Genome Build 37.

The probe sequences for the CpG sites were selected from the Infinium HM450 array (Illumina, Inc., San Diego, Calif.). Design principles for the probes are shown in FIG. 1. Two probes were used for targets having greater than 4 CpG sites, including a completely methylated probe (having a G nucleotides that complements the C position of each CpG site) and completely unmethylated probe (having an A nucleotide that complements the U that is expected to result from bisulfite conversion of each of the C positions of a CpG site) as shown in FIG. 1. In contrast, only one probe was used for targets having 4 or fewer CpG sites (the probe includes degenerate nucleotide R, complementary to U or C, at the C position of each CpG site).

Isolation and Extraction of cfDNA from Plasma

Plasma samples were obtained from human blood draws. Cell free DNA (cfDNA) was extracted using the QIAamp Circulating Nucleic Acid Kit (Qiagen, Hilden, Germany). Targeted ctDNA methylation sequencing was carried out according to the workflow shown in FIG. 2, and as set forth below in the context of evaluating titration and detection sensitivity.

Titration and Detection Sensitivity

As described above, NA12878 genomic DNA was purchased from Coriell Institute (Coriell Institute, Camden, N.J.), and LS1034 genomic DNA was purchased from ATCC (ATCC, Manassas, Va.). Genomic DNA was fragmented using Covaris M200 (Covaris, Woburn, Mass.) and size-selected to 130-250 bp using BluePippin (Sage Science, Beverly, Mass.) to simulate the size distribution of cfDNA. DNA quantification was performance using Quant-iT™ PicoGreen® dsDNA Assay Kit (ThermoFisher Scientific, Grand Island, N.Y.). 10%, 1%, or 0.1% LS1034 DNA was spiked into NA12878 DNA background to make the DNA mixtures. 30 ng of each mixture, 100% NA12878, or 100% LS1034 DNA was used in library preparation. Three replicated libraries were generated for each titration level. A set of six replicates of NA12878 was used as the baseline reference genome.

Extracted cfDNA or sheared and size-selected genomic DNA was bisulfite treated and purified using EZ DNA Methylation-Lightning Kit (Zymo Research, Irvine, Calif.).

Bisulfite-seq Libraries were prepared using the Accel-NGS® Methyl-Seq DNA Library Kit (Swift Biosciences, Ann Arbor, Mich.).

Targeted capture was carried out on the bisulfite-seq libraries using probes that were complementary to fragments having the CpG sites listed in Table I or Table II. Capture probes were synthesized and biotinylated at Illumina, Inc. Target capture was performed using Illumina TruSight™ Rapid Capture Kit according to manufacturer's instructions except that customized capture probes were used, and hybridization and wash steps were performed at 48 C.

The products of the capture step were sequenced on an Illumina HiSeq 2500 Sequencer using 2×100 cycle runs, with four samples in rapid run mode, according to manufacturer's instructions.

Bioinformatic Analysis

FASTQ sequences were demultiplexed followed by in silico demethylation whereby all C's on read 1 were converted to T's and all G's on read 2 were converted to A's. Subsequently, these “demethylated” FASTQ sequences were aligned using BWA (v 0.7.10-r789) to an index comprising a “demethylated” hg19 genome. BWA alignment is described in Li and Durbin (2010) Fast and accurate long-read alignment with Burrows-Wheeler Transform. Bioinformatics, Epub. [PMID: 20080505], which is incorporated herein by reference. Following alignment, the “demethylated” FASTQ sequences were replaced with the original FASTQ sequences. Methylation levels were calculated as the fraction of ‘C’ bases at a target CpG site out of ‘C’+‘T’ total bases.

After calculation of methylation levels at each CpG site for each sample and replicate, coverage-weighted methylation scores were calculated as follows.

(1) The methylation level at each CpG site was normalized by subtracting the mean methylation level in baseline and dividing by the standard deviation of methylation levels in the baseline to obtain a per-site z-score. Specifically, the normalized methylation difference at each CpG site was determined according to the formula:

$Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$

where Z_(i) represents a normalized methylation difference for a particular site identified as i, χ_(i) represents the methylation level at site i in the test genomic DNA, μ_(i) represents the mean methylation level at site i in the reference genome, and σ_(i) represents the standard deviation of methylation levels at site i in the reference genomic DNA.

(2) The z-score for each CpG site i (Z_(i)) was converted into the probability of observing such a z-score or greater by converting the z-score into a one-sided p-value (p_(i)). Probabilities were calculated assuming a normal distribution, although other distributions (e.g., t-distribution or binomial distribution) may be used as well.

(3) The p-value at each CpG site was weighted by multiplying the p-value at each CpG site i (p_(i)) by the coverage observed at the CpG site (w_(i)), and a coverage-weighted methylation score (MS) was determined by combining the weighted p-values according to the formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}\; {\ln \left( {w_{i}p_{i}} \right)}}}$

where p_(i) represents the one-sided p-value at site i, k represents the total number of sites, and w_(i) represents the coverage at site i.

Results

A titration experiment was performed to demonstrate analytical sensitivity using a colorectal cancer cell line LS1034 and a normal cell line NA12878. Namely, targeted ctDNA methylation sequencing was performed in triplicates using the combined Pan-Cancer and CRC panels on 0.1%, 1%, and 10% titrations of LS1034 into NA12878 along with pure LS1034 and pure NA12878. For each of the 15 sample replicates, the coverage-weighted methylation scores were calculated using the normal NA12878 samples as the baseline (FIG. 4). The results indicate that the within sample variation is far less than the variation between titration levels. In particular, the clear separation of the 0.1% titration of LS1034 into NA12878 from the NA12878 sample indicates the assay and accompanying coverage-weighted methylation score can achieve a 0.1% limit of detection (see inset of FIG. 4).

Similar to the results in Example I, results obtained using the methods of this example provide high sensitivity evaluation of the cumulative effect of multiple affected CpG sites across the genome. By providing an alternative method for detecting methylation patterns, the methods of this example can provide a more sensitive cancer diagnosis than methods relying on detection of somatic mutations.

Example III Clinical Performance of ctDNA Methylation-Based Cancer Detection Using Normalized Coverage-Weighted Methylation Score Differences

This example evaluates clinical sensitivity and specificity of the methylation-based cancer detection in circulating tumor DNA (ctDNA) using normalized coverage weighted methylation score differences. As noted above, the assay described in this example can be useful for cancer screening, monitoring disease progression, or evaluating a patient's response to a therapeutic treatment.

Targeted Capture Probe Design

For this study, the two targeted methylation panels described in Example I were pooled together. The Pan-Cancer Panel targets 9,921 affected CpG sites in 20 major cancer types as selected from The Cancer Genome Atlas Database. The CpG sites included in the Pan-Cancer Panel are listed in Table I. The CRC Panel targets 1,162 affected CpG sites in colorectal cancer. The CpG sites included in the CRC Panel are listed in Table II. The combined CpG sites listed in Table I and Table II refer to Genome Build 37.

The probe sequences for the CpG sites were selected from the Infinium HM450 array (Illumina, Inc., San Diego, Calif.). Design principles for the probes are shown in FIG. 1. Two probes were used for targets having greater than 4 CpG sites, including a completely methylated probe (having a G nucleotides that complements the C position of each CpG site) and completely unmethylated probe (having an A nucleotide that complements the U that is expected to result from bisulfite conversion of each of the C positions of a CpG site) as shown in FIG. 1. In contrast, only one probe was used for targets having 4 or fewer CpG sites (the probe includes degenerate nucleotide R, complementary to U or C, at the C position of each CpG site).

Blood Sample Collection and Processing

Cancer patients were recruited at MD Anderson Cancer Center (Houston, Tex.). A total of 70 blood samples collected from 63 late stage cancer patients of three cancer types were used in this study (n=30 for colorectal cancer (CRC), n=14 for breast cancer (BRCA), n=19 for lung cancer). Four CRC patients had blood samples collected at multiple time points. Three breast cancer samples and one colorectal cancer sample failed sample quality control and therefore were excluded from the analysis, resulting in the final set of 66 cancer samples (36 CRC, 11 BRCA, and 19 lung), representing 59 different patients (29 CRC, 11 BRCA, and 19 lung). A total of 65 normal blood samples were collected from healthy subjects to be used as baseline methylation controls (20), training controls (20) and testing controls (25) as described herein.

Plasma was separated by centrifugation at 1600 G for 10 minutes. The supernatant was transferred to 15 mL centrifuge tubes and centrifuged at room temperature for 10 minutes at 3000 G. The supernatant was transferred to a fresh 15 mL centrifuge tube and stored in a freezer (−80° C.) and shipped on dry ice. Plasma samples from healthy donors were obtained from BioreclamationIVT (Westbury, N.Y.). All samples were de-identified.

Isolation and Extraction of cfDNA from Plasma

Cell free DNA (cfDNA) was extracted using the QIAamp Circulating Nucleic Acid Kit (Qiagen, Hilden, Germany). Targeted ctDNA methylation sequencing was carried out according to the workflow shown in FIG. 2, and as set forth below in the context of evaluating titration and detection sensitivity.

Targeted Bisulfite Sequencing Library Preparation and Sequencing cfDNA was bisulfite treated and purified using EZ DNA Methylation-Lightning Kit (Zymo Research, Irvine, Calif.).

Whole genome amplification of bisulfite-converted DNA was performed using Accel-NGS® Methyl-Seq DNA Library Kit (Swift Biosciences, Ann Arbor, Mich.).

Targeted capture was carried out on the bisulfite-seq libraries using probes that were complementary to fragments having the CpG sites listed in Tables I and II. Capture probes were synthesized and biotinylated at Illumina, Inc. (San Diego, Calif.). Target capture was performed using Illumina TruSight™ Rapid Capture Kit according to manufacturer's instructions. Hybridization and wash conditions were modified to yield optimal capture efficiency.

The products of the capture step were sequenced on an Illumina Hiseq2500 Sequencer using 2×100 cycle runs, with four samples in rapid run mode, according to manufacturer's instructions.

Bioinformatic Analysis

FASTQ sequences were demultiplexed followed by in silico demethylation whereby all C's on read 1 were converted to T's and all G's on read 2 were converted to A's. Subsequently, these “demethylated” FASTQ sequences were aligned using BWA (v 0.7.10-r789) to an index comprising a “demethylated” hg19 genome. BWA alignment is described in Li and Durbin (2010) Fast and accurate long-read alignment with Burrows-Wheeler Transform. Bioinformatics, Epub. [PMID: 20080505], which is incorporated herein by reference. Following alignment, the “demethylated” FASTQ sequences were replaced with the original FASTQ sequences. Methylation levels were calculated as the fraction of ‘C’ bases at a target CpG site out of ‘C’+‘T’ total bases.

After calculation of methylation levels at each CpG site for each sample and replicate, coverage-weighted methylation scores were calculated as follows.

(1) Methylation scores were initially determined for the training set of 20 normal genomic DNA samples. First, a normalized methylation difference (z-score) at a particular site i (e.g., CpG site) was determined according to the formula:

$Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$

wherein Z_(i) represents a normalized methylation difference for a particular site identified as i, χ_(i) represents the methylation level at site i in a member of the training set of normal genomic DNA, μ_(i) represents the mean methylation level at site i in the baseline samples, and σ_(i) represents the standard deviation of methylation levels at site i in the baseline samples.

(2) The z-score for each CpG site i (Z_(i)) was converted into the probability of observing such a z-score or greater by converting the z-score into a one-sided p-value (p_(i)). Probabilities were calculated assuming a normal distribution, although other distributions (e.g., t-distribution or binomial distribution) may be used as well.

(3) The p-value at each CpG site was weighted by multiplying the p-value at each CpG site i (p_(i)) by the coverage observed at the CpG site (w_(i)), and a coverage-weighted methylation score (MS) was determined by combining the weighted p-values according to the formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}\; {\ln \left( {w_{i}p_{i}} \right)}}}$

wherein p_(i) represents the one-sided p-value at site i, k represents the total number of sites, and w_(i) represents the significance, for instance coverage, of the site i.

(4) Statistical analysis of the training set methylation scores was then performed. The mean methylation score (μ_(MS)) and standard deviation of methylation scores (σ_(MS)) in the training set of normal genomic DNA were calculated, characterizing the distribution of the methylation score in a normal population.

(5) Next, methylation scores were determined for the 66 cancer genomic DNA samples and 25 testing controls. First, a normalized methylation difference (z-score) at each CpG site was determined according to the formula:

$Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$

where Z_(i) represents a normalized methylation difference for a particular site identified as i, χ_(i) represents the methylation level at site i in the test genomic DNA, μ_(i) represents the mean methylation level at site i in the reference genome, and σ_(i) represents the standard deviation of methylation levels at site i in the reference genomic DNA.

(6) The z-score for each CpG site i (Z_(i)) was converted into the probability of observing such a z-score or greater by converting the z-score into a one-sided p-value (p_(i)). Probabilities were calculated assuming a normal distribution, although other distributions (e.g., t-distribution or binomial distribution) may be used as well.

(7) The p-value at each CpG site was weighted by multiplying the p-value at each CpG site i (p_(i)) by the coverage observed at the CpG site (w_(i)), and a coverage-weighted methylation score (MS) was determined by combining the weighted p-values according to the formula:

${MS} = {{- 2}{\sum\limits_{i = 1}^{k}\; {\ln \left( {w_{i}p_{i}} \right)}}}$

where p_(i) represents the one-sided p-value at site i, k represents the total number of sites, and w_(i) represents the coverage at site i.

(8) Finally, the methylation scores of the test genomic DNA samples were evaluated against the distribution of methylation scores determined for the training set population, represented by the mean methylation score (μ_(MS)) and standard deviation of methylation scores (σ_(MS)) for the training set of normal genomic DNA. The number of standard deviations between the methylation score for the test genomic DNA and the methylation score mean (μ_(MS)) of the training set of normal genomic DNA was determined according to the formula:

$Z_{MS} = \frac{{MS} - µ_{MS}}{\sigma_{MS}}$

wherein Z_(MS) represents a normalized methylation score difference, MS represents the methylation score of the test sample, μ_(MS) represents the mean methylation score for the training set of normal genomic DNA, and σ_(MS) represents the standard deviation of methylation scores for the training set of normal genomic DNA. A Z_(MS) value greater than 3 standard deviations was used as a threshold to identify cancer samples.

Results

As noted above, the purpose of this experiment was to evaluate the clinical performance of the normalized coverage-weighted methylation score difference algorithm, including its clinical sensitivity and specificity. The 66 cancer samples and 25 normal samples were subjected to the methylation score analysis as described herein, including determining the z-score for each of the CpG sites listed in Tables I and II, converting the z-score into a one-sided p-value based on a normal distribution assumption, weighting the p-values by coverage, and aggregating the individual weighted p-values into a single methylation score using the Fisher formula. The resulting methylation scores were used to distinguish the cancer samples from the normal samples. FIGS. 5 and 6 show that the normalized coverage-weighted methylation score difference algorithm was able to detect 34 out of 36 CRC samples (94.4% sensitivity), 8 out of 11 BRCA samples (72.7% sensitivity), and 10 out of 19 lung cancer samples (52.6% sensitivity). The algorithm exhibited 100% specificity, having correctly identified all 25 of the testing control samples as normal.

Results obtained using the methods of this example provide highly sensitive and specific evaluation of the cumulative effect of multiple affected CpG sites across the genome. By providing an alternative method for detecting methylation patterns, the methods of this example can provide a more sensitive and specific cancer diagnosis than methods relying on detection of somatic mutations.

Example IV Clinical Performance of Cancer Type Classification Method Based on Average Methylation Levels Across Preselected Subsets of Methylation Sites

This example evaluates the clinical sensitivity of a method for cancer type classification based on average methylation levels across preselected subsets of CpG methylation sites referred to herein as “hypermethylated” sites. The assay described in this example can be useful for identifying the source of tumor in circulating cell-free DNA.

Correlation of Methylation Profiles Between Plasma and Tissue DNA Samples

As an initial inquiry, we set out to determine how well the methylation profiles of circulating tumor DNA (ctDNA) isolated from plasma samples correlated to those of DNA isolated from tumor tissues. A high degree of correlation would lend credence to the idea that methylation profiles of cfDNA can be used to classify the tumor of origin. To this end, we compared the methylation profiles of the colorectal, breast and lung cancer samples that were detected in Example III to the average methylation profiles for each of the 32 cancer types from TCGA (The Cancer Genomic Atlas) that had a minimum of 30 cancer samples in the database. The methylation profiles were determined substantially as described in Examples I-III and consisted of methylation levels at 9,242 CpG sites (poorly performing methylation sites from the original CpG panels were filtered out to improve accuracy).

The comparison was performed in a pairwise manner between each cancer-positive plasma sample from Example III and each of the 32 cancer type from TCGA, resulting in correlation coefficients ranging from 0 to 1. The correlations were plotted as a two-dimensional correlation map, which is shown in FIG. 7. The darker areas of the map correspond to higher correlations, whereas the lighter areas of the map signify lower correlations. The observed correlations between the methylation profiles were generally highest for the matching tumor types. For example, in the breast cancer samples from plasma, the correlation was highest to the breast cancer tissue (breast invasive carcinoma), and lower in all other tumor tissue types. Similarly, for the CRC plasma samples, the correlation was highest to colon and rectum tissues (e.g., colon adenocarcinoma, esophageal carcinoma, rectum adenocarcinoma and stomach adenocarcinoma). The correlation was less pronounced in the lung cancer samples.

Development and Testing of Cancer Type Classification

Having determined that there is a significant correlation between methylation profiles of ctDNA and DNA from tumor tissues, we proceeded to develop and test a cancer type classification method in silico.

First, we identified 24 cancer types with more than 100 samples in the TCGA database. For each of these types, we created a list of “hypermethylated” sites, which were defined as sites having a mean methylation level (across samples) in the top 3% across the entire panel and greater than 6% in terms of absolute values.

Given a test sample, we determined its cancer types in a three-step process. First, for each of the 24 cancer types, the methylation levels for each of the “hypermethylated” sites on the list were determined as described in Examples I-III. Next, the average methylation level across the “hypermethylated” sites were calculated for each of the 24 cancer types. Finally, each of the 24 cancer types was ranked by their average methylation levels across the “hypermethylated” sites and classified the test sample by the cancer type with the highest average methylation level.

We then proceeded to back-test the method on each of the TCGA tissue samples that was used to generate the lists of “hypermethylated” sites. Accuracy of the method was defined as the ratio of the number of cancer samples of a particular type that were identified correctly to the total number of samples of that cancer type. Results of this analysis are shown in FIG. 8. As one can easily see from this figure, 22 out of 24 cancer types were classified with over 75% accuracy. Indeed, many of the cancer types were correctly identified about 90% of the time or better. Only two types—esophageal carcinoma and testicular germ cell tumors—failed to cross the 75% threshold.

Cancer Type Classification of Plasma Samples

The 52 plasma samples correctly identified as cancer samples in Example III (34 CRC, 8 BRCA, and 10 lung) were subjected to the cancer type classification analysis as described above. Results of this analysis are shown in FIG. 9. The cancer classification algorithm correctly identified 28 out of 34 CRC samples (82%), 7 out of 8 BRCA samples (88%) and 7 out of 10 lung cancer samples (70%). These results demonstrate that the cancer type classification method described herein may be used with a high clinical sensitivity to identify the tissue of origin in ctDNA from plasma samples.

Throughout this application various publications, patents or patent applications have been referenced. The disclosures of these publications in their entireties are hereby incorporated by reference in this application in order to more fully describe the state of the art to which this invention pertains.

The term “comprising” is intended herein to be open-ended, including not only the recited elements, but further encompassing any additional elements.

Although the invention has been described with reference to the examples provided above, it should be understood that various modifications can be made without departing from the invention. Accordingly, the invention is limited only by the claims. 

What is claimed is:
 1. A method for distinguishing an aberrant methylation level for DNA for a sample containing DNA from a plurality of different cell types, comprising (a) providing a sample comprising a mixture of genomic DNA from a plurality of different cell types from an individual test organism, thereby providing test genomic DNA; (b) detecting methylation states for a plurality of CpG sites in the test genomic DNA; (c) determining the coverage at each of the CpG sites for the detecting of the methylation states; (d) providing methylation states for the plurality of CpG sites in reference genomic DNA from at least one reference individual; (e) determining, for each of the CpG sites, the methylation difference between the test genomic DNA and the reference genomic DNA, thereby providing a normalized methylation difference for each CpG site; and (f) weighting the normalized methylation difference for each CpG site by the coverage at each of the CpG sites, thereby determining an aggregate coverage-weighted normalized methylation difference score.
 2. The method of claim 1, wherein the sample comprises circulating tumor DNA and circulating non-tumor DNA.
 3. The method of claim 1, wherein the sample comprises cell free DNA from blood.
 4. The method of claim 1, wherein the individual test organism is a pregnant female and the test genomic DNA comprises genomic DNA derived from somatic cells of the female and genomic DNA derived from somatic cells of prenatal offspring of the female.
 5. The method of claim 1, wherein the providing of the sample in step (a) comprises targeted selection of a subset of genomic DNA fragments comprising a set of predetermined target CpG sites.
 6. The method of claim 5, wherein the providing of the sample in step (a) further comprises treating the subset of genomic DNA fragments with bisulfate.
 7. The method of claim 1, wherein the detecting in step (b) comprises a sequencing technique that serially distinguishes nucleotides in the test genomic DNA.
 8. The method of claim 1, comprising (I) repeating steps (a) through (f) using a second test genomic DNA provided from a sample comprising a mixture of genomic DNA from a plurality of different cell types from the individual test organism, and using the same reference genomic DNA from the at least one reference individual, and (II) determining whether or not a change has occurred in in the aggregate coverage-weighted normalized methylation difference score between the test genomic DNA and the second test genomic DNA.
 9. The method of claim 1, wherein the normalized methylation difference at a particular CpG site is determined according to $Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$ wherein Z_(i) represents a normalized methylation difference for a particular CpG site identified as i, χ_(i) represents the methylation level at CPG site i in the test genomic DNA, μ_(i) represents the mean methylation level at CpG site i in the reference genome, and σ_(i) represents the standard deviation of methylation levels at CpG site i in the reference genomic DNA.
 10. The method of claim 9, wherein the aggregate coverage-weighted normalized methylation difference score (represented as A) is determined according to $A = \frac{\sum_{i = 1}^{k}\; {w_{i}Z_{i}}}{\sqrt{\sum_{i = 1}^{k}\; w_{i}^{2}}}$ wherein w_(i) represents the coverage at CpG site i, and k represents the total number of CpG sites.
 11. A method for distinguishing an aberrant methylation level for DNA from a first cell type, the method comprising (a) providing, for a plurality of CpG sites in baseline genomic DNA from two or more normal individual baseline organisms, a mean methylation level and a standard deviation of methylation level for each CpG site in the baseline genomic DNA; (b) providing a test data set comprising: methylation states for the plurality of CpG sites from a first test genomic DNA from an individual test organism, wherein the CpG sites are derived from a sample, (c) determining, for each of the CpG sites, the methylation difference between the first test genomic DNA and the baseline genomic DNA, thereby providing a normalized methylation difference for each CpG site; and (d) converting the normalized methylation difference for each CpG site into a one-sided p-value; (e) determining an aggregate methylation score for the combination of one-sided p-values for each CpG site for the first test genomic DNA.
 12. The method of claim 11, wherein (a) comprises providing methylation states for the plurality of CpG sites in the baseline genomic DNA from the two or more normal individual organisms, and determining, for each of the CpG sites, the mean methylation level and standard deviation of methylation level for the baseline genomic DNA.
 13. The method of claim 11, further comprising providing a second test data set comprising: methylation states for the plurality of CpG sites from a second test genomic DNA from the individual test organism, and wherein the CpG sites are derived from a sample; determining, for each of the CpG sites, the methylation difference between the second test genomic DNA and the baseline genomic DNA, thereby providing a normalized methylation difference for each CpG site for the second test genomic DNA; and converting the normalized methylation difference for each CpG site for the second test genomic DNA into a one-sided p-value; determining an aggregate methylation score for the combination of one-sided p-values for each CpG site for the second test genomic DNA; and comparing the aggregate methylation score of the first test genomic DNA and the second test genomic DNA to determine whether or not a change has occurred in the aggregate methylation score between the first and second test genomic DNA.
 14. The method of claim 11, further comprising: (f) providing a training data set comprising: methylation states for the plurality of CpG sites from training genomic DNA from two or more normal individual training organisms, wherein the CpG sites are derived from a plurality of different cell types from the normal individual training organisms; (g) determining, for each of the CpG sites, the methylation difference between each training genomic DNA from the normal individual training organisms and the baseline genomic DNA, thereby providing a normalized methylation difference for each CpG site for each training genomic DNA; (h) converting the normalized methylation difference for each CpG site into a one-sided p-value; and (i) determining an aggregate methylation score for the combination of one-sided p-values for each CpG site for each training genomic DNA; (j) using the aggregate methylation score for each training genomic DNA to calculate a mean aggregate methylation score and a standard deviation of the aggregate methylation scores for the training genomic DNA, to result in a distribution of the aggregate methylation scores for the training genomic DNA; and (k) evaluating the aggregate methylation score of the first test genomic DNA against the distribution of the aggregate methylation scores for the training genomic DNA.
 15. The method of claim 11, wherein the normalized methylation difference at a particular CpG site is determined according to the formula: $Z_{i} = \frac{\chi_{i} - µ_{i}}{\sigma_{i}}$ wherein Z_(i) represents a normalized methylation difference for a particular CpG site identified as i, χ_(i) represents the methylation level at CPG site i in the first test genomic DNA or the training genomic DNA, μ_(i) represents the mean methylation level at CpG site i in the baseline genome, and σ_(i) represents the standard deviation of methylation levels at CpG site i in the baseline genomic DNA.
 16. The method of claim 14, wherein the evaluating of step (k) determines a normalized methylation score difference according to the formula: $Z_{MS} = \frac{{MS} - µ_{MS}}{\sigma_{MS}}$ wherein Z_(MS) represents a normalized methylation score difference, MS represents the aggregate methylation score of the first test genomic DNA, μ_(MS) represents the mean methylation score for the training set of normal genomic DNA, and σ_(MS) represents the standard deviation of aggregate methylation scores for the training set of normal genomic DNA.
 17. The method of claim 11, wherein the aggregate methylation score (MS) is determined according to the formula: ${MS} = {{- 2}{\sum\limits_{i = 1}^{k}\; {\ln \left( p_{i} \right)}}}$ wherein p_(i) represents the one-sided p-value at site i, and k represents the total number of CpG sites.
 18. The method of claim 11, wherein the aggregate methylation score (MS) is determined according to the formula: ${MS} = {{- 2}{\sum\limits_{i = 1}^{k}\; {\ln \left( {w_{i}p_{i}} \right)}}}$ wherein p_(i) represents the one-sided p-value at site i, k represents the total number of CpG sites, and w_(i) represents coverage of the site i.
 19. The method of claim 11, wherein the sample from the individual test organism comprises circulating tumor DNA and circulating non-tumor DNA.
 20. The method of claim 11, wherein the sample comprises cell-free DNA from blood.
 21. The method of claim 11, wherein the individual test organism is a pregnant female and the first test genomic DNA comprises genomic DNA derived from somatic cells of the female and genomic DNA derived from somatic cells of prenatal offspring of the female.
 22. The method of claim 11, wherein the providing of the sample in step (b) comprises targeted selection of a subset of genomic DNA fragments comprising a set of predetermined target CpG sites.
 23. The method of claim 11, wherein the providing of the sample in step (b) further comprises treating the subset of genomic DNA fragments with bisulfite.
 24. The method of claim 11, wherein the providing of the sample in step (b) comprises detecting methylation states for the CpG sites.
 25. The method of claim 11, wherein the detecting methylation states for the CpG sites comprises a sequencing technique that sequentially identifies nucleotides in the first test genomic DNA.
 26. The method of claim 16, wherein the first test genomic DNA is classified as having an aberrant methylation level if the value of Z_(MS) is greater than
 3. 27. A method for identifying a cancer present in an individual organism, comprising (a) providing a data set comprising: methylation states for a plurality of CpG sites i from genomic DNA from clinical samples known to comprise a specific cancer; (b) identifying hypermethylated CpG sites i characteristic for a cancer type, comprising: (i) determining a mean methylation level for each CpG site i in the genomic DNA of the clinical samples known to comprise the specific cancer, (ii) determining which CpG sites i meet a first threshold, a second threshold, or a combination thereof, wherein determining the first threshold comprises (1) determining the absolute value of the mean methylation level of each CpG site i; (2) ranking the mean methylation levels for each CpG site i from lowest to highest, and (3) selecting those CpG sites i having a mean methylation level at a percentile rank that is greater than or equivalent to a first preselected value, and wherein determining the second threshold comprises (1) determining the absolute value of the mean methylation level of each CpG site i; and (2) selecting those CpG sites i having a mean methylation level that is greater than a second preselected value; (iii) compiling a list of hypermethylated sites that are characteristic for the cancer type, (c) repeating (a) and (b) for each specific cancer, to result in a plurality of lists of hypermethylated sites that are characteristic for additional cancer types; (d) providing a test data set comprising: a methylation level for each hypermethylated site from a test genomic DNA from an individual test organism, wherein the hypermethylated sites are from one of the lists of hypermethylated sites that is characteristic for a cancer type identified in steps (b) and (c); (e) averaging the methylation level of each of the hypermethylated sites to result in a single average methylation level for the test genomic DNA for the cancer type identified in steps (b) and (c); (f) repeating step (e) for each cancer type, to result in an average methylation level for each cancer type; (g) ranking the average methylation levels for each cancer type from lowest to highest, wherein the cancer type corresponding to the highest average methylation level is the cancer present in the individual test organism.
 28. The method of claim 27, wherein the first preselected value is a percentile rank that is greater than or equivalent to
 97. 29. The method of claim 27, wherein the second preselected value is greater than 6%.
 30. The method of claim 27, wherein the test genomic DNA from the individual test organism comprises circulating tumor DNA and circulating non-tumor DNA.
 31. The method of claim 27, wherein the test genomic DNA comprises cell-free DNA from blood.
 32. The method of claim 27, wherein the providing of the test data set in step (d) comprises targeted selection of a subset of genomic DNA fragments comprising a set of predetermined target CpG sites.
 33. The method of claim 27, wherein the providing the test data set in step (d) further comprises treating the subset of genomic DNA fragments with bisulfite.
 34. The method of claim 27, wherein the providing of the test data set in step (d) comprises detecting methylation states for the CpG sites.
 35. The method of claim 27, wherein the detecting methylation states for the CpG sites comprises a sequencing technique that sequentially identifies nucleotides in the first test genomic DNA. 